2023
|
| Chen, Sining; Shi, Yilei; Xiong, Zhitong; Zhu, Xiao Xiang HTC-DC Net: Monocular Height Estimation From Single Remote Sensing Images Journal Article IEEE Transactions on Geoscience and Remote Sensing, 61 , pp. 1-18, 2023, ISSN: 0196-2892, (The work is jointly supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ”AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ”national center of excellence ML4Earth” (grant number: 50EE2201C), by the Munich Center for Machine Learning and by the TUM Georg Nemetschek Institute for Artificial Intelligence for the Built World as part of the AI4TWINNING project. The contributions of S. Chen was carried out in part during the time when he was jointly affiliated with the Technical University of Munich and the German Aerospace Center supported by a DAAD scholarship.). Links | BibTeX | Tags: @article{RN263,
title = {HTC-DC Net: Monocular Height Estimation From Single Remote Sensing Images},
author = {Sining Chen and Yilei Shi and Zhitong Xiong and Xiao Xiang Zhu},
url = {https://ieeexplore.ieee.org/ielx7/36/10006360/10294289.pdf?tp=&arnumber=10294289&isnumber=10006360&ref=},
doi = {10.1109/tgrs.2023.3321255},
issn = {0196-2892},
year = {2023},
date = {2023-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {61},
pages = {1-18},
note = {The work is jointly supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ”AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ”national center of excellence ML4Earth” (grant number: 50EE2201C), by the Munich Center for Machine Learning and by the TUM Georg Nemetschek Institute for Artificial Intelligence for the Built World as part of the AI4TWINNING project. The contributions of S. Chen was carried out in part during the time when he was jointly affiliated with the Technical University of Munich and the German Aerospace Center supported by a DAAD scholarship.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Daixin, Zhao; Konrad, Heidler; Milad, Asgarimehr; Caroline, Arnold; Tianqi, Xiao; Jens, Wickert; Xiang, Zhu Xiao; Lichao, Mou DDM-Former: Transformer networks for GNSS reflectometry global ocean wind speed estimation Journal Article Remote Sensing of Environment, 294 , pp. 113629, 2023, ISSN: 0034-4257, (This work is jointly supported by the Hermann von Helmholtz-
Gemeinschaft Deutscher Forschungszentren e.V. project “Artificial Intelligence
for GNSS Reflectometry: Novel Remote Sensing of Ocean and
Atmosphere (AI4GNSSR)” [grant number: ZT-I-PF-5-091], by the
German Federal Ministry of Education and Research (BMBF) in the
framework of the international future AI lab “AI4EO – Artificial Intelligence
for Earth Observation: Reasoning, Uncertainties, Ethics and
Beyond” [grant number: 01DD20001] and by the German Federal
Ministry of Economics and Technology in the framework of the “national
center of excellence ML4Earth” [grant number: 50EE2201C]. CA
was funded by Helmholtz Association's Initiative and Networking Fund
through Helmholtz AI [grant number: ZT-I-PF-5-01]. This work used
resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its
Scientific Steering Committee (WLA) under project ID AIM and was
supported by Helmholtz AI computing resources (HAICORE) of the
Helmholtz Association's Initiative and Networking Fund through
Helmholtz AI. The datasets used in this study are available free of charge
and we thank the scientific teams associated with the CYGNSS mission at
NASA and the University of Michigan, and ERA5 reanalysis estimates at
the European Centre for Medium-Range Weather Forecasts (ECMWF).). Links | BibTeX | Tags: Deep learning, GNSS reflectometry, Ocean wind speed, Transformer networks @article{RN264,
title = {DDM-Former: Transformer networks for GNSS reflectometry global ocean wind speed estimation},
author = {Zhao Daixin and Heidler Konrad and Asgarimehr Milad and Arnold Caroline and Xiao Tianqi and Wickert Jens and Zhu Xiao Xiang and Mou Lichao},
url = {https://www.sciencedirect.com/science/article/pii/S0034425723001803},
doi = {https://doi.org/10.1016/j.rse.2023.113629},
issn = {0034-4257},
year = {2023},
date = {2023-01-01},
journal = {Remote Sensing of Environment},
volume = {294},
pages = {113629},
note = {This work is jointly supported by the Hermann von Helmholtz-
Gemeinschaft Deutscher Forschungszentren e.V. project “Artificial Intelligence
for GNSS Reflectometry: Novel Remote Sensing of Ocean and
Atmosphere (AI4GNSSR)” [grant number: ZT-I-PF-5-091], by the
German Federal Ministry of Education and Research (BMBF) in the
framework of the international future AI lab “AI4EO – Artificial Intelligence
for Earth Observation: Reasoning, Uncertainties, Ethics and
Beyond” [grant number: 01DD20001] and by the German Federal
Ministry of Economics and Technology in the framework of the “national
center of excellence ML4Earth” [grant number: 50EE2201C]. CA
was funded by Helmholtz Association's Initiative and Networking Fund
through Helmholtz AI [grant number: ZT-I-PF-5-01]. This work used
resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its
Scientific Steering Committee (WLA) under project ID AIM and was
supported by Helmholtz AI computing resources (HAICORE) of the
Helmholtz Association's Initiative and Networking Fund through
Helmholtz AI. The datasets used in this study are available free of charge
and we thank the scientific teams associated with the CYGNSS mission at
NASA and the University of Michigan, and ERA5 reanalysis estimates at
the European Centre for Medium-Range Weather Forecasts (ECMWF).},
keywords = {Deep learning, GNSS reflectometry, Ocean wind speed, Transformer networks},
pubstate = {published},
tppubtype = {article}
}
|
| de Gélis, Iris; Saha, Sudipan; Shahzad, Muhammad; Corpetti, Thomas; Lefèvre, Sébastien; Zhu, Xiao Xiang Deep Unsupervised Learning for 3D ALS Point Clouds Change Detection Journal Article Computer Methods and Programs in Biomedicine, 240 , pp. 107721, 2023, ISSN: 0169-2607, (Iris de Gélis is also with the Chair of Data Science in Earth Observation as a Beyond Fellow in the International Future LabAI4EO, Technical University of Munich (TUM), Germany. Her work is also partly funded by the CNES, Toulouse, France.arXiv:2305.03529v1 [eess.IV] 5 May 2023
This work was granted access to the HPC resources of IDRIS under the allocation 2021-AD011011754R1 made by GENCI. The research is also funded by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning,Uncertainties, Ethics and Beyond” (grant number: 01DD20001).). Links | BibTeX | Tags: Band selection, Data gravitation, Human brain, Medical hyperspectral images @article{RN265,
title = {Deep Unsupervised Learning for 3D ALS Point Clouds Change Detection},
author = {Iris de Gélis and Sudipan Saha and Muhammad Shahzad and Thomas Corpetti and Sébastien Lefèvre and Xiao Xiang Zhu},
url = {https://www.sciencedirect.com/science/article/pii/S0169260723003875},
doi = {https://doi.org/10.1016/j.cmpb.2023.107721},
issn = {0169-2607},
year = {2023},
date = {2023-01-01},
journal = {Computer Methods and Programs in Biomedicine},
volume = {240},
pages = {107721},
note = {Iris de Gélis is also with the Chair of Data Science in Earth Observation as a Beyond Fellow in the International Future LabAI4EO, Technical University of Munich (TUM), Germany. Her work is also partly funded by the CNES, Toulouse, France.arXiv:2305.03529v1 [eess.IV] 5 May 2023
This work was granted access to the HPC resources of IDRIS under the allocation 2021-AD011011754R1 made by GENCI. The research is also funded by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning,Uncertainties, Ethics and Beyond” (grant number: 01DD20001).},
keywords = {Band selection, Data gravitation, Human brain, Medical hyperspectral images},
pubstate = {published},
tppubtype = {article}
}
|
| Jens, Hoffmann Eike; Karam, Abdulahhad; Xiang, Zhu Xiao Using social media images for building function classification Journal Article Cities, 133 , pp. 104107, 2023, ISSN: 0264-2751, (The work is jointly supported by the European Research Council
(ERC) under the European Union's Horizon 2020 research and innovation
programme (grant agreement No. [ERC-2016-StG-714087],
Acronym: So2Sat), by the Helmholtz Association through the Framework
of the Helmholtz Excellent Professorship “Data Science in Earth
Observation - Big Data Fusion for Urban Research”(grant number: W2-
W3-100), by the German Federal Ministry of Education and Research
(BMBF) in the framework of the international future AI lab “AI4EO –
Artificial Intelligence for Earth Observation: Reasoning, Uncertainties,
Ethics and Beyond” (grant number: 01DD20001) and by German Federal
Ministry for Economic Affairs and Climate Action in the framework of
the “national center of excellence ML4Earth” (grant number:
50EE2201C).). Links | BibTeX | Tags: Big data analytics, Building function classification, Social media image analysis, Urban land use @article{RN266,
title = {Using social media images for building function classification},
author = {Hoffmann Eike Jens and Abdulahhad Karam and Zhu Xiao Xiang},
url = {https://www.sciencedirect.com/science/article/pii/S0264275122005467},
doi = {https://doi.org/10.1016/j.cities.2022.104107},
issn = {0264-2751},
year = {2023},
date = {2023-01-01},
journal = {Cities},
volume = {133},
pages = {104107},
note = {The work is jointly supported by the European Research Council
(ERC) under the European Union's Horizon 2020 research and innovation
programme (grant agreement No. [ERC-2016-StG-714087],
Acronym: So2Sat), by the Helmholtz Association through the Framework
of the Helmholtz Excellent Professorship “Data Science in Earth
Observation - Big Data Fusion for Urban Research”(grant number: W2-
W3-100), by the German Federal Ministry of Education and Research
(BMBF) in the framework of the international future AI lab “AI4EO –
Artificial Intelligence for Earth Observation: Reasoning, Uncertainties,
Ethics and Beyond” (grant number: 01DD20001) and by German Federal
Ministry for Economic Affairs and Climate Action in the framework of
the “national center of excellence ML4Earth” (grant number:
50EE2201C).},
keywords = {Big data analytics, Building function classification, Social media image analysis, Urban land use},
pubstate = {published},
tppubtype = {article}
}
|
| Fan, Fan; Shi, Yilei; Guggemos, Tobias; Zhu, Xiao Xiang Hybrid Quantum-Classical Convolutional Neural Network Model for Image Classification Journal Article IEEE Transactions on Neural Networks and Learning Systems, pp. 1-15, 2023, ISSN: 2162-237X, (Manuscript received 2 December 2022; revised 15 May 2023 and 2 August 2023; accepted 31 August 2023. The work of Xiao Xiang Zhu was supported in part by the German Federal Ministry of Education and Research (BMBF) in the framework of the International Future AI Lab “AI4EO—Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” under Grant 01DD20001; and in part by the Munich Center for Machine Learning (MCML). (Corresponding author: Xiao Xiang Zhu.) Fan Fan is with the Chair of Data Science in Earth Observation (SiPEO),Technical University of Munich (TUM), 80333 München, Germany, and also with the Remote Sensing Technology Institute (IMF), German AerospaceCenter (DLR), 82234 Weßling, Germany. Yilei Shi is with the School of Engineering and Design, Technical University of Munich (TUM), 80333 München, Germany. Tobias Guggemos is with the Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Weßling, Germany.Xiao Xiang Zhu is with the Chair of Data Science in Earth Observation(SiPEO), Technical University of Munich (TUM), 80333 München, Germany(e-mail: xiaoxiang.zhu.ieee@gmail.com).Color versions of one or more figures in this article are available at https://doi.org/10.1109/TNNLS.2023.3312170.Digital Object Identifier 10.1109/TNNLS.2023.3312170). Links | BibTeX | Tags: @article{RN267,
title = {Hybrid Quantum-Classical Convolutional Neural Network Model for Image Classification},
author = {Fan Fan and Yilei Shi and Tobias Guggemos and Xiao Xiang Zhu},
doi = {10.1109/tnnls.2023.3312170},
issn = {2162-237X},
year = {2023},
date = {2023-01-01},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
pages = {1-15},
note = {Manuscript received 2 December 2022; revised 15 May 2023 and 2 August 2023; accepted 31 August 2023. The work of Xiao Xiang Zhu was supported in part by the German Federal Ministry of Education and Research (BMBF) in the framework of the International Future AI Lab “AI4EO—Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” under Grant 01DD20001; and in part by the Munich Center for Machine Learning (MCML). (Corresponding author: Xiao Xiang Zhu.) Fan Fan is with the Chair of Data Science in Earth Observation (SiPEO),Technical University of Munich (TUM), 80333 München, Germany, and also with the Remote Sensing Technology Institute (IMF), German AerospaceCenter (DLR), 82234 Weßling, Germany. Yilei Shi is with the School of Engineering and Design, Technical University of Munich (TUM), 80333 München, Germany. Tobias Guggemos is with the Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Weßling, Germany.Xiao Xiang Zhu is with the Chair of Data Science in Earth Observation(SiPEO), Technical University of Munich (TUM), 80333 München, Germany(e-mail: xiaoxiang.zhu.ieee@gmail.com).Color versions of one or more figures in this article are available at https://doi.org/10.1109/TNNLS.2023.3312170.Digital Object Identifier 10.1109/TNNLS.2023.3312170},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Gawlikowski, Jakob; Saha, Sudipan; Niebling, Julia; Zhu, Xiao Xiang Handling unexpected inputs: incorporating source-wise out-of-distribution detection into SAR-optical data fusion for scene classification Journal Article EURASIP Journal on Advances in Signal Processing, 2023 (1), 2023, ISSN: 1687-6180, (Open Access funding enabled and organized by Projekt DEAL. This research was funded by the German Aerospace Center (DLR) and the international AI4EO FutureLab. The work of X. Zhu is jointly supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat), by the Helmholtz Association through the Framework of Helmholtz AI (Grant number: ZT-I-PF-5-01) - Local Unit “Munich Unit @Aeronautics, Space and Transport (MASTr)” and Helmholtz Excellent Professorship “Data Science in Earth Observation - Big Data Fusion for Urban Research”(Grant number: W2-W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (Grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the “national center of excellence ML4Earth” (Grant number: 50EE2201C).). Links | BibTeX | Tags: @article{RN268,
title = {Handling unexpected inputs: incorporating source-wise out-of-distribution detection into SAR-optical data fusion for scene classification},
author = {Jakob Gawlikowski and Sudipan Saha and Julia Niebling and Xiao Xiang Zhu},
doi = {10.1186/s13634-023-01008-z},
issn = {1687-6180},
year = {2023},
date = {2023-01-01},
journal = {EURASIP Journal on Advances in Signal Processing},
volume = {2023},
number = {1},
note = {Open Access funding enabled and organized by Projekt DEAL. This research was funded by the German Aerospace Center (DLR) and the international AI4EO FutureLab. The work of X. Zhu is jointly supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat), by the Helmholtz Association through the Framework of Helmholtz AI (Grant number: ZT-I-PF-5-01) - Local Unit “Munich Unit @Aeronautics, Space and Transport (MASTr)” and Helmholtz Excellent Professorship “Data Science in Earth Observation - Big Data Fusion for Urban Research”(Grant number: W2-W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (Grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the “national center of excellence ML4Earth” (Grant number: 50EE2201C).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Gawlikowski, Jakob; Tassi, Cedrique Rovile Njieutcheu; Ali, Mohsin; Lee, Jongseok; Humt, Matthias; Feng, Jianxiang; Kruspe, Anna; Triebel, Rudolph; Jung, Peter; Roscher, Ribana; Shahzad, Muhammad; Yang, Wen; Bamler, Richard; Zhu, Xiao Xiang A survey of uncertainty in deep neural networks Journal Article Artificial Intelligence Review, 56 (S1), pp. 1513-1589, 2023, ISSN: 0269-2821, (Open Access funding enabled and organized by Projekt DEAL. This work is in part supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO—Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (Grant Number: 01DD20001).). Links | BibTeX | Tags: @article{RN269,
title = {A survey of uncertainty in deep neural networks},
author = {Jakob Gawlikowski and Cedrique Rovile Njieutcheu Tassi and Mohsin Ali and Jongseok Lee and Matthias Humt and Jianxiang Feng and Anna Kruspe and Rudolph Triebel and Peter Jung and Ribana Roscher and Muhammad Shahzad and Wen Yang and Richard Bamler and Xiao Xiang Zhu},
doi = {10.1007/s10462-023-10562-9},
issn = {0269-2821},
year = {2023},
date = {2023-01-01},
journal = {Artificial Intelligence Review},
volume = {56},
number = {S1},
pages = {1513-1589},
note = {Open Access funding enabled and organized by Projekt DEAL. This work is in part supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO—Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (Grant Number: 01DD20001).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Guo, Jianhua; Liu, Zhiheng; Zhu, Xiao Xiang Assessing the macro-scale patterns of urban tree canopy cover in Brazil using high-resolution remote sensing images Journal Article Sustainable Cities and Society, 100 , 2023, ISSN: 22106707, (This work was supported in part by the Sino-German (CSC-DAAD) Postdoc Scholarship Program under Grant 202006255045, in part by the German Federal Ministry of Education and Research(BMBF) in the framework of the international future AI Laboratory ‘‘AI4EO-Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’’ under Grant 01DD20001, and in part by the German Federal Ministry of Economics and Technology in the framework of the ‘‘National Center of excellence ML4Earth’’ under Grant 50EE2201C.). Links | BibTeX | Tags: @article{RN270,
title = {Assessing the macro-scale patterns of urban tree canopy cover in Brazil using high-resolution remote sensing images},
author = {Jianhua Guo and Zhiheng Liu and Xiao Xiang Zhu},
doi = {10.1016/j.scs.2023.105003},
issn = {22106707},
year = {2023},
date = {2023-01-01},
journal = {Sustainable Cities and Society},
volume = {100},
note = {This work was supported in part by the Sino-German (CSC-DAAD) Postdoc Scholarship Program under Grant 202006255045, in part by the German Federal Ministry of Education and Research(BMBF) in the framework of the international future AI Laboratory ‘‘AI4EO-Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’’ under Grant 01DD20001, and in part by the German Federal Ministry of Economics and Technology in the framework of the ‘‘National Center of excellence ML4Earth’’ under Grant 50EE2201C.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Heidler, Konrad; Mou, Lichao; Hu, Di; Jin, Pu; Li, Guangyao; Gan, Chuang; Wen, Ji-Rong; Zhu, Xiao Xiang Self-supervised audiovisual representation learning for remote sensing data Journal Article International Journal of Applied Earth Observation and Geoinformation, 116 , pp. 103130, 2023, ISSN: 1569-8432, (This research would not have been possible without the countless contributors to the radio aporee ::: maps project.
Further, we acknowledge Google for providing imagery from Google Earth for research purposes.
This work is supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ‘‘AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’’ (grant number: 01DD20001), by the German Federal Ministry for Economic Affairs and Climate Action in the framework of the ‘‘national center of excellence ML4Earth’’ (grant number: 50EE2201C), by the Fundamental Research Funds for the Central Universities, China, by the Research Funds of Renmin University of China (NO. 2021030200), and by the Beijing Outstanding Young Scientist Program (NO. BJJWZYJH012019100020098), Public Computing Cloud, Renmin University of China.). Links | BibTeX | Tags: Self-supervised learning Multi-modal learning Representation learning Audiovisual dataset @article{RN271,
title = {Self-supervised audiovisual representation learning for remote sensing data},
author = {Konrad Heidler and Lichao Mou and Di Hu and Pu Jin and Guangyao Li and Chuang Gan and Ji-Rong Wen and Xiao Xiang Zhu},
url = {https://www.sciencedirect.com/science/article/pii/S1569843222003181},
doi = {https://doi.org/10.1016/j.jag.2022.103130},
issn = {1569-8432},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {116},
pages = {103130},
note = {This research would not have been possible without the countless contributors to the radio aporee ::: maps project.
Further, we acknowledge Google for providing imagery from Google Earth for research purposes.
This work is supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ‘‘AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’’ (grant number: 01DD20001), by the German Federal Ministry for Economic Affairs and Climate Action in the framework of the ‘‘national center of excellence ML4Earth’’ (grant number: 50EE2201C), by the Fundamental Research Funds for the Central Universities, China, by the Research Funds of Renmin University of China (NO. 2021030200), and by the Beijing Outstanding Young Scientist Program (NO. BJJWZYJH012019100020098), Public Computing Cloud, Renmin University of China.},
keywords = {Self-supervised learning Multi-modal learning Representation learning Audiovisual dataset},
pubstate = {published},
tppubtype = {article}
}
|
| Heidler, Konrad; Mou, Lichao; Loebel, Erik; Scheinert, Mirko; Lefèvre, Sébastien; Zhu, Xiao A Deep Active Contour Model for Delineating Glacier Calving Fronts Journal Article 2023, (K. Heidler, L. Mou and X. Zhu are with the Chair of Data Science in Earth Observation (SiPEO), Department of Aerospace and Geodesy, School of Engineering and Design, Technical University of Munich (TUM), 80333 Munich E-mails: k.heidler@tum.de; lichao.mou@tum.de; xiaoxiang.zhu@tum.de E. Loebel and M. Scheinert are with the Institut fu ̈r Planetare Geoda ̈sie, Technische Universita ̈t Dresden, 01069 Dresden, Germany. E-mails: erik.loebel@tu-dresden.de, mirko.scheinert@tu-dresden.de S. Lefèvre is with IRISA UMR 6074, Université Bretagne Sud, 56000 Vannes, France. E-mail: sebastien.lefevre@univ-ubs.fr
A preliminary version of this study was presented at IGARSS 2022. This work is supported by the Helmholtz Association through the Helmholtz Information and Data Science Incubator project “Artificial Intelligence for Cold Regions”, Acronym AI-Core, by Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI [grant number: ZT-I-PF-5-01] – Local Unit “Munich Unit @Aeronautics, Space and Transport (MASTr)”, by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (Grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ”national center of excellence ML4Earth” (grant number: 50EE2201C). (Corresponding authors: Xiao Xiang Zhu, Lichao Mou.)). BibTeX | Tags: @article{RN272,
title = {A Deep Active Contour Model for Delineating Glacier Calving Fronts},
author = {Konrad Heidler and Lichao Mou and Erik Loebel and Mirko Scheinert and Sébastien Lefèvre and Xiao Zhu},
year = {2023},
date = {2023-01-01},
note = {K. Heidler, L. Mou and X. Zhu are with the Chair of Data Science in Earth Observation (SiPEO), Department of Aerospace and Geodesy, School of Engineering and Design, Technical University of Munich (TUM), 80333 Munich E-mails: k.heidler@tum.de; lichao.mou@tum.de; xiaoxiang.zhu@tum.de E. Loebel and M. Scheinert are with the Institut fu ̈r Planetare Geoda ̈sie, Technische Universita ̈t Dresden, 01069 Dresden, Germany. E-mails: erik.loebel@tu-dresden.de, mirko.scheinert@tu-dresden.de S. Lefèvre is with IRISA UMR 6074, Université Bretagne Sud, 56000 Vannes, France. E-mail: sebastien.lefevre@univ-ubs.fr
A preliminary version of this study was presented at IGARSS 2022. This work is supported by the Helmholtz Association through the Helmholtz Information and Data Science Incubator project “Artificial Intelligence for Cold Regions”, Acronym AI-Core, by Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI [grant number: ZT-I-PF-5-01] – Local Unit “Munich Unit @Aeronautics, Space and Transport (MASTr)”, by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (Grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ”national center of excellence ML4Earth” (grant number: 50EE2201C). (Corresponding authors: Xiao Xiang Zhu, Lichao Mou.)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Hermann, Martin; Saha, Sudipan; Zhu, Xiao Xiang Filtering Specialized Change in a Few-Shot Setting Journal Article IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16 , pp. 1185-1196, 2023, ISSN: 1939-1404, (Manuscript received 11 October 2022; revised 8 December 2022; accepted14 December 2022. Date of publication 9 January 2023; date of current version12 January 2023. This work was supported in part by the Munich Aerospacee.V. scholarship as part of the research group IMonitor, in part by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” under Grant 01DD20001, and in part by the German Federal Ministry for Economic Affairs and Climate Action in the framework of the “National Center of Excellence ML4Earth” under Grant50EE2201C. (Corresponding author: Xiao Xiang Zhu.)The authors are with the Chair of Data Science in Earth Observation,Technical University of Munich, 85521 Ottobrunn, Germany (e-mail: mar-tin.hermann@tum.de; sudipan.saha@tum.de; xiaoxiang.zhu.ieee@gmail.com). Code will be available at https://gitlab.lrz.de/ai4eo/cd/-/blob/main/fewShotFilteringCd.Digital Object Identifier 10.1109/JSTARS.2022.3231915). Links | BibTeX | Tags: @article{RN273,
title = {Filtering Specialized Change in a Few-Shot Setting},
author = {Martin Hermann and Sudipan Saha and Xiao Xiang Zhu},
doi = {10.1109/jstars.2022.3231915},
issn = {1939-1404},
year = {2023},
date = {2023-01-01},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
volume = {16},
pages = {1185-1196},
note = {Manuscript received 11 October 2022; revised 8 December 2022; accepted14 December 2022. Date of publication 9 January 2023; date of current version12 January 2023. This work was supported in part by the Munich Aerospacee.V. scholarship as part of the research group IMonitor, in part by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” under Grant 01DD20001, and in part by the German Federal Ministry for Economic Affairs and Climate Action in the framework of the “National Center of Excellence ML4Earth” under Grant50EE2201C. (Corresponding author: Xiao Xiang Zhu.)The authors are with the Chair of Data Science in Earth Observation,Technical University of Munich, 85521 Ottobrunn, Germany (e-mail: mar-tin.hermann@tum.de; sudipan.saha@tum.de; xiaoxiang.zhu.ieee@gmail.com). Code will be available at https://gitlab.lrz.de/ai4eo/cd/-/blob/main/fewShotFilteringCd.Digital Object Identifier 10.1109/JSTARS.2022.3231915},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Hu, Jingliang; Liu, Rong; Hong, Danfeng; Camero, Andrés; Yao, Jing; Schneider, Mathias; Kurz, Franz; Segl, Karl; Zhu, Xiao Xiang MDAS: a new multimodal benchmark dataset for remote sensing Journal Article Earth System Science Data, 15 (1), pp. 113-131, 2023, ISSN: 1866-3516, (It is impossible to accomplish this work without the help of a lot of colleagues. We would like to thank Chao-nan Ji, Marianne Jilge, and Uta Heiden for their inspiring discus-sions; Martin Bachmann and Stefanie Holzwarth for sharing with us their experience on preparing hyperspectral images; and last but not least, Rudolf Richter for supporting us with the software AT-COR.Financial support.This research has been supported by the German Federal Ministry of Economics and Technology in the framework of the “national center of excellence ML4Earth” (grant no.50EE2201C), by the Helmholtz Association through the framework of Helmholtz AI (grant no. ZT-I-PF-5-01) – Local Unit “MunichUnit @Aeronautics, Space and Transport (MASTr)” and Helmholtz Excellent Professorship “Data Science in Earth Observation – BigData Fusion for Urban Research” (grant no. W2-W3-100), and by the German Federal Ministry of Education and Research (BMBF)in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (grant no. 01DD20001)). Links | BibTeX | Tags: @article{RN274,
title = {MDAS: a new multimodal benchmark dataset for remote sensing},
author = {Jingliang Hu and Rong Liu and Danfeng Hong and Andrés Camero and Jing Yao and Mathias Schneider and Franz Kurz and Karl Segl and Xiao Xiang Zhu},
doi = {10.5194/essd-15-113-2023},
issn = {1866-3516},
year = {2023},
date = {2023-01-01},
journal = {Earth System Science Data},
volume = {15},
number = {1},
pages = {113-131},
note = {It is impossible to accomplish this work without the help of a lot of colleagues. We would like to thank Chao-nan Ji, Marianne Jilge, and Uta Heiden for their inspiring discus-sions; Martin Bachmann and Stefanie Holzwarth for sharing with us their experience on preparing hyperspectral images; and last but not least, Rudolf Richter for supporting us with the software AT-COR.Financial support.This research has been supported by the German Federal Ministry of Economics and Technology in the framework of the “national center of excellence ML4Earth” (grant no.50EE2201C), by the Helmholtz Association through the framework of Helmholtz AI (grant no. ZT-I-PF-5-01) – Local Unit “MunichUnit @Aeronautics, Space and Transport (MASTr)” and Helmholtz Excellent Professorship “Data Science in Earth Observation – BigData Fusion for Urban Research” (grant no. W2-W3-100), and by the German Federal Ministry of Education and Research (BMBF)in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (grant no. 01DD20001)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Huang, Wei; Shi, Yilei; Xiong, Zhitong; Zhu, Xiao AdaptMatch: Adaptive Matching for Semi-supervised Binary Segmentation of Remote Sensing Images Journal Article IEEE Transactions on Geoscience and Remote Sensing, PP , pp. 1-1, 2023, (This project is supported by the German Research Foundation (DFG GZ: ZH 498/18-1; Project number: 519016653), by the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) based on a resolution of the German Bundestag (grant number: 67KI32002B; Acronym: EKAPEx), and by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ”AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (grant number: 01DD20001).
Corresponding author: Xiao Xiang Zhu. W. Huang, Z. Xiong, and X. Zhu are with the Chair of Data Science in Earth Observation, Technical University of Munich, 80333 Munich, Germany (e-mail: w2wei.huang, zhitong.xiong, xiaoxiang.zhu@tum.de). Y. Shi is with the School of Engineering and Design, Technical University of Munich (TUM), 80333 Munich, Germany (e-mail: yilei.shi@tum.de).). Links | BibTeX | Tags: @article{RN275,
title = {AdaptMatch: Adaptive Matching for Semi-supervised Binary Segmentation of Remote Sensing Images},
author = {Wei Huang and Yilei Shi and Zhitong Xiong and Xiao Zhu},
doi = {10.1109/TGRS.2023.3332490},
year = {2023},
date = {2023-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {PP},
pages = {1-1},
note = {This project is supported by the German Research Foundation (DFG GZ: ZH 498/18-1; Project number: 519016653), by the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) based on a resolution of the German Bundestag (grant number: 67KI32002B; Acronym: EKAPEx), and by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ”AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (grant number: 01DD20001).
Corresponding author: Xiao Xiang Zhu. W. Huang, Z. Xiong, and X. Zhu are with the Chair of Data Science in Earth Observation, Technical University of Munich, 80333 Munich, Germany (e-mail: w2wei.huang, zhitong.xiong, xiaoxiang.zhu@tum.de). Y. Shi is with the School of Engineering and Design, Technical University of Munich (TUM), 80333 Munich, Germany (e-mail: yilei.shi@tum.de).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Jianhua, Guo; Qingsong, Xu; Yue, Zeng; Zhiheng, Liu; Xiang, Zhu Xiao Nationwide urban tree canopy mapping and coverage assessment in Brazil from high-resolution remote sensing images using deep learning Journal Article ISPRS Journal of Photogrammetry and Remote Sensing, 198 , pp. 1-15, 2023, ISSN: 0924-2716, (This work was supported in part by the Sino-German (CSC-DAAD) Postdoc Scholarship Program under Grant 202006255045, in part by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI Laboratory ‘‘AI4EO-Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’’ under Grant 01DD20001, in part by the German Federal Ministry of Economics and Technology in the framework of the ‘‘National Center of excellence ML4Earth’’ under Grant 50EE2201C, and in part by the Helmholtz Excellent Professorship‘‘Data Science in Earth Observation Big Data Fusion for Urban Research’’ under Grant W2-W3-100.). Links | BibTeX | Tags: Brazil, Remote sensing, Semi-supervised learning, Urban ecosystem services, Urban tree canopy @article{RN276,
title = {Nationwide urban tree canopy mapping and coverage assessment in Brazil from high-resolution remote sensing images using deep learning},
author = {Guo Jianhua and Xu Qingsong and Zeng Yue and Liu Zhiheng and Zhu Xiao Xiang},
url = {https://www.sciencedirect.com/science/article/pii/S0924271623000461},
doi = {https://doi.org/10.1016/j.isprsjprs.2023.02.007},
issn = {0924-2716},
year = {2023},
date = {2023-01-01},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {198},
pages = {1-15},
note = {This work was supported in part by the Sino-German (CSC-DAAD) Postdoc Scholarship Program under Grant 202006255045, in part by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI Laboratory ‘‘AI4EO-Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’’ under Grant 01DD20001, in part by the German Federal Ministry of Economics and Technology in the framework of the ‘‘National Center of excellence ML4Earth’’ under Grant 50EE2201C, and in part by the Helmholtz Excellent Professorship‘‘Data Science in Earth Observation Big Data Fusion for Urban Research’’ under Grant W2-W3-100.},
keywords = {Brazil, Remote sensing, Semi-supervised learning, Urban ecosystem services, Urban tree canopy},
pubstate = {published},
tppubtype = {article}
}
|
| Kondmann, Lukas; Saha, Sudipan; Zhu, Xiao Xiang SemiSiROC: Semisupervised Change Detection With Optical Imagery and an Unsupervised Teacher Model Journal Article IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16 , pp. 3879-3891, 2023, (Manuscript received 17 October 2022; revised 26 January 2023 and 24 February 2023; accepted 29 March 2023. Date of publication 20 April 2023; date of current version 28 April 2023. This work was supported in part by the Helmholtz Association through the joint research school “Munich School for Data Science - MUDS” and Helmholtz Excellent Professorship “Data Science in Earth Observation - Big Data Fusion for Urban Research” under Grant W2-W3-100, and in part by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” under Grant 01DD20001. (Corresponding author: Xiao Xiang Zhu.) Lukas Kondmann is with the Remote Sensing Technology Institute, German Aerospace Center (DLR), 82234 Wessling, Germany, and also with the Data Science in Earth Observation, Technical University of Munich, 85521 Ottobrunn, Germany (e-mail: Lukas.Kondmann@dlr.de). Sudipan Saha is with the Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi 110016, India (e-mail: sudipan.saha@scai.iitd.ac.in). Xiao Xiang Zhu is with the Data Science in Earth Observation, Technical University of Munich, 85521 Ottobrunn, Germany (e-mail: xiaoxiang.zhu.ieee@gmail.com).). Links | BibTeX | Tags: Earth Training Optical sensors Optical imaging Data models Optical filters Uncertainty Change detection (CD) multitemporal optical images semisupervised unsupervised @article{RN277,
title = {SemiSiROC: Semisupervised Change Detection With Optical Imagery and an Unsupervised Teacher Model},
author = {Lukas Kondmann and Sudipan Saha and Xiao Xiang Zhu},
doi = {10.1109/JSTARS.2023.3268104},
year = {2023},
date = {2023-01-01},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
volume = {16},
pages = {3879-3891},
note = {Manuscript received 17 October 2022; revised 26 January 2023 and 24 February 2023; accepted 29 March 2023. Date of publication 20 April 2023; date of current version 28 April 2023. This work was supported in part by the Helmholtz Association through the joint research school “Munich School for Data Science - MUDS” and Helmholtz Excellent Professorship “Data Science in Earth Observation - Big Data Fusion for Urban Research” under Grant W2-W3-100, and in part by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” under Grant 01DD20001. (Corresponding author: Xiao Xiang Zhu.) Lukas Kondmann is with the Remote Sensing Technology Institute, German Aerospace Center (DLR), 82234 Wessling, Germany, and also with the Data Science in Earth Observation, Technical University of Munich, 85521 Ottobrunn, Germany (e-mail: Lukas.Kondmann@dlr.de). Sudipan Saha is with the Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi 110016, India (e-mail: sudipan.saha@scai.iitd.ac.in). Xiao Xiang Zhu is with the Data Science in Earth Observation, Technical University of Munich, 85521 Ottobrunn, Germany (e-mail: xiaoxiang.zhu.ieee@gmail.com).},
keywords = {Earth Training Optical sensors Optical imaging Data models Optical filters Uncertainty Change detection (CD) multitemporal optical images semisupervised unsupervised},
pubstate = {published},
tppubtype = {article}
}
|
| Li, Tian; Dawson, Geoffrey J; Chuter, Stephen J; Bamber, Jonathan L Grounding line retreat and tide-modulated ocean channels at Moscow University and Totten Glacier ice shelves, East Antarctica Journal Article The Cryosphere, 17 (2), pp. 1003-1022, 2023, ISSN: 1994-0424, (Tian Li received funding from the ChinaScholarship Council (CSC) – University of Bristol joint-fundedPhD scholarship. Jonathan L. Bamber and Stephen J. Chuter received funding from the European Research Council (Global-Mass; grant no. 694188). Stephen J. Chuter also received funding from the European Space Agency (ESA) as part of the Climate Change Initiative (CCI) fellowship (ESA ESRIN/contract no. 4000133466/20/I/NB). Jonathan L. Bamber also received fund-ing from the German Federal Ministry of Education and Research(BMBF) in the framework of the international future lab AI4EO (grant no. 01DD20001). We thank Michiel van den Broeke and Peter Kuipers Munneke for providing the RACMO 2.3 data. We thank Chad A. Greene, Bernd Scheuchl, Bert Wouters, Rob Bingham, Pietro Milillo, and one anonymous reviewer for providing valuable comments on this study. Financial support. This research has been supported by the European Research Council, H2020 European Research Council(GlobalMass (grant no. 694188)), the European Space Agency (grant no. 4000133466/20/I/NB), and the Bundesministerium für Bildung und Forschung (grant no. 01DD20001).). Links | BibTeX | Tags: @article{RN278,
title = {Grounding line retreat and tide-modulated ocean channels at Moscow University and Totten Glacier ice shelves, East Antarctica},
author = {Tian Li and Geoffrey J Dawson and Stephen J Chuter and Jonathan L Bamber},
doi = {10.5194/tc-17-1003-2023},
issn = {1994-0424},
year = {2023},
date = {2023-01-01},
journal = {The Cryosphere},
volume = {17},
number = {2},
pages = {1003-1022},
note = {Tian Li received funding from the ChinaScholarship Council (CSC) – University of Bristol joint-fundedPhD scholarship. Jonathan L. Bamber and Stephen J. Chuter received funding from the European Research Council (Global-Mass; grant no. 694188). Stephen J. Chuter also received funding from the European Space Agency (ESA) as part of the Climate Change Initiative (CCI) fellowship (ESA ESRIN/contract no. 4000133466/20/I/NB). Jonathan L. Bamber also received fund-ing from the German Federal Ministry of Education and Research(BMBF) in the framework of the international future lab AI4EO (grant no. 01DD20001). We thank Michiel van den Broeke and Peter Kuipers Munneke for providing the RACMO 2.3 data. We thank Chad A. Greene, Bernd Scheuchl, Bert Wouters, Rob Bingham, Pietro Milillo, and one anonymous reviewer for providing valuable comments on this study. Financial support. This research has been supported by the European Research Council, H2020 European Research Council(GlobalMass (grant no. 694188)), the European Space Agency (grant no. 4000133466/20/I/NB), and the Bundesministerium für Bildung und Forschung (grant no. 01DD20001).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Qingyu, Li; Lichao, Mou; Yuansheng, Hua; Yilei, Shi; Sining, Chen; Yao, Sun; Xiang, Zhu Xiao 3DCentripetalNet: Building height retrieval from monocular remote sensing imagery Journal Article International Journal of Applied Earth Observation and Geoinformation, 120 , pp. 103311, 2023, ISSN: 1569-8432, (The work is jointly supported by the Excellence Strategy of the Federal Government and the Länder through the TUM Innovation Network EarthCare, Germany, the Helmholtz Association through the Framework of the Helmholtz Excellent Professorship ‘‘Data Science in Earth Observation - Big Data Fusion for Urban Research’’(grant number: W2-W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ‘‘AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’’ (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ‘‘national center of excellence ML4Earth’’ (grant number: 50EE2201C).). Links | BibTeX | Tags: Building footprint generation, Building height retrieval, Height estimation, Monocular imagery @article{RN279,
title = {3DCentripetalNet: Building height retrieval from monocular remote sensing imagery},
author = {Li Qingyu and Mou Lichao and Hua Yuansheng and Shi Yilei and Chen Sining and Sun Yao and Zhu Xiao Xiang},
url = {https://www.sciencedirect.com/science/article/pii/S1569843223001334},
doi = {https://doi.org/10.1016/j.jag.2023.103311},
issn = {1569-8432},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {120},
pages = {103311},
note = {The work is jointly supported by the Excellence Strategy of the Federal Government and the Länder through the TUM Innovation Network EarthCare, Germany, the Helmholtz Association through the Framework of the Helmholtz Excellent Professorship ‘‘Data Science in Earth Observation - Big Data Fusion for Urban Research’’(grant number: W2-W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ‘‘AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’’ (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ‘‘national center of excellence ML4Earth’’ (grant number: 50EE2201C).},
keywords = {Building footprint generation, Building height retrieval, Height estimation, Monocular imagery},
pubstate = {published},
tppubtype = {article}
}
|
| Qingyu, Li; Sebastian, Krapf; Yilei, Shi; Xiang, Zhu Xiao SolarNet: A convolutional neural network-based framework for rooftop solar potential estimation from aerial imagery Journal Article International Journal of Applied Earth Observation and Geoinformation, 116 , pp. 103098, 2023, ISSN: 1569-8432, (The work is jointly supported by the TUM Innovation Network Earth Care, by European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat), by the Helmholtz Association, Germany through the Framework of the Helmholtz Excellent Professorship ‘‘Data Science in Earth Observation - Big Data Fusion for Urban Research’’(grant number: W2-W3-100), by the German Federal Ministry of Education and Research (BMBF), Germany in the framework of the international future AI lab ‘‘AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’’ (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action, Germany in the framework of the ‘‘national center of excellence ML4Earth’’ (grant number: 50EE2201C). This work is a part of the project ‘‘Investigation of building cases using AI’’ funded by Bavarian State Ministry of Finance and Regional Identity (StMFH) and the Bavarian Agency for Digitization, High-Speed Internet and Surveying, Germany.). Links | BibTeX | Tags: Convolutional neural network, Remote sensing, Renewable energy, Roof segments and orientations, Solar potential @article{RN280,
title = {SolarNet: A convolutional neural network-based framework for rooftop solar potential estimation from aerial imagery},
author = {Li Qingyu and Krapf Sebastian and Shi Yilei and Zhu Xiao Xiang},
url = {https://www.sciencedirect.com/science/article/pii/S1569843222002862},
doi = {https://doi.org/10.1016/j.jag.2022.103098},
issn = {1569-8432},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {116},
pages = {103098},
note = {The work is jointly supported by the TUM Innovation Network Earth Care, by European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat), by the Helmholtz Association, Germany through the Framework of the Helmholtz Excellent Professorship ‘‘Data Science in Earth Observation - Big Data Fusion for Urban Research’’(grant number: W2-W3-100), by the German Federal Ministry of Education and Research (BMBF), Germany in the framework of the international future AI lab ‘‘AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’’ (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action, Germany in the framework of the ‘‘national center of excellence ML4Earth’’ (grant number: 50EE2201C). This work is a part of the project ‘‘Investigation of building cases using AI’’ funded by Bavarian State Ministry of Finance and Regional Identity (StMFH) and the Bavarian Agency for Digitization, High-Speed Internet and Surveying, Germany.},
keywords = {Convolutional neural network, Remote sensing, Renewable energy, Roof segments and orientations, Solar potential},
pubstate = {published},
tppubtype = {article}
}
|
| Runmin, Dong; Lixian, Zhang; Weijia, Li; Shuai, Yuan; Lin, Gan; Juepeng, Zheng; Haohuan, Fu; Lichao, Mou; Xiang, Zhu Xiao An adaptive image fusion method for Sentinel-2 images and high-resolution images with long-time intervals Journal Article International Journal of Applied Earth Observation and Geoinformation, 121 , pp. 103381, 2023, ISSN: 1569-8432, (This research was supported in part by the National Key Research
and Development Plan of China (Grant No. 2020YFB0204800), National
Natural Science Foundation of China (Grant No. T2125006), Jiangsu
Innovation Capacity Building Program (Project No. BM2022028), the
German Federal Ministry of Education and Research (BMBF) in the
framework of the international future AI lab "AI4EO – Artificial Intelligence
for Earth Observation: Reasoning, Uncertainties, Ethics and
Beyond" (grant number: 01DD20001), and Shuimu Tsinghua Scholar
Project.). Links | BibTeX | Tags: Deep learning, High-resolution remote sensing, Multi-source image, Spatial resolution, Super-resolution @article{RN281,
title = {An adaptive image fusion method for Sentinel-2 images and high-resolution images with long-time intervals},
author = {Dong Runmin and Zhang Lixian and Li Weijia and Yuan Shuai and Gan Lin and Zheng Juepeng and Fu Haohuan and Mou Lichao and Zhu Xiao Xiang},
url = {https://www.sciencedirect.com/science/article/pii/S1569843223002054},
doi = {https://doi.org/10.1016/j.jag.2023.103381},
issn = {1569-8432},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {121},
pages = {103381},
note = {This research was supported in part by the National Key Research
and Development Plan of China (Grant No. 2020YFB0204800), National
Natural Science Foundation of China (Grant No. T2125006), Jiangsu
Innovation Capacity Building Program (Project No. BM2022028), the
German Federal Ministry of Education and Research (BMBF) in the
framework of the international future AI lab "AI4EO – Artificial Intelligence
for Earth Observation: Reasoning, Uncertainties, Ethics and
Beyond" (grant number: 01DD20001), and Shuimu Tsinghua Scholar
Project.},
keywords = {Deep learning, High-resolution remote sensing, Multi-source image, Spatial resolution, Super-resolution},
pubstate = {published},
tppubtype = {article}
}
|
| Shanyu, Zhou; Lichao, Mou; Yuansheng, Hua; Lixian, Zhang; Hermann, Kaufmann; Xiang, Zhu Xiao Can we use deep learning models to identify the functionality of plastics from space? Journal Article International Journal of Applied Earth Observation and Geoinformation, 123 , pp. 103491, 2023, ISSN: 1569-8432, (The work is jointly supported by the Helmholtz Association through the Framework of the Helmholtz Excellent Professorship ‘Data Science in Earth Observation—Big Data Fusion for Urban Research’ (Grant number: W2-W3-100) and through the Helmholtz Imaging Platform (HIP) project ‘WeMonitor’, and by the German Federal Ministry of Education
and Research (BMBF) in the framework of the international
future AI lab ‘AI4EO—Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’ (Grant number: 01DD20001). Finally, the authors would like to thank the anonymous reviewers for their efforts and constructive suggestions to improve the quality of this article.). Links | BibTeX | Tags: Deep learning, Environmental management, Plastic detection, Plastic functionality, Sentinel-2 @article{RN282,
title = {Can we use deep learning models to identify the functionality of plastics from space?},
author = {Zhou Shanyu and Mou Lichao and Hua Yuansheng and Zhang Lixian and Kaufmann Hermann and Zhu Xiao Xiang},
url = {https://www.sciencedirect.com/science/article/pii/S1569843223003151},
doi = {https://doi.org/10.1016/j.jag.2023.103491},
issn = {1569-8432},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {123},
pages = {103491},
note = {The work is jointly supported by the Helmholtz Association through the Framework of the Helmholtz Excellent Professorship ‘Data Science in Earth Observation—Big Data Fusion for Urban Research’ (Grant number: W2-W3-100) and through the Helmholtz Imaging Platform (HIP) project ‘WeMonitor’, and by the German Federal Ministry of Education
and Research (BMBF) in the framework of the international
future AI lab ‘AI4EO—Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’ (Grant number: 01DD20001). Finally, the authors would like to thank the anonymous reviewers for their efforts and constructive suggestions to improve the quality of this article.},
keywords = {Deep learning, Environmental management, Plastic detection, Plastic functionality, Sentinel-2},
pubstate = {published},
tppubtype = {article}
}
|
| Song, Qian; Albrecht, Conrad; Xiong, Zhitong; Zhu, Xiao Biomass Estimation and Uncertainty Quantification From Tree Height Journal Article IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, PP , pp. 1-14, 2023, (Manuscript received 24 February 2023; revised 19 April 2023; accepted 22 April 2023. Date of current version 5 June 2023. This work was jointly supported in part by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO—Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” under Grant 01DD20001 and in part by the German Federal Ministry for Economic Affairs and Climate Action in the framework of the “National Center of Excellence ML4Earth” under Grant 50EE2201C. (Corresponding author: Xiao Xiang Zhu.) Qian Song, Zhitong Xiong, and Xiao Xiang Zhu are with the Chair of Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), 85521 Munich, Germany (e-mail: qian.song@tum.de; zhitong.xiong@tum.de; xiaoxiang.zhu@tum.de). Conrad M. Albrecht is with the Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Munich, Germany (e-mail: Conrad. Albrecht@dlr.de).
Digital Object Identifier 10.1109/JSTARS.2023.3271186 1https://ec.europa.eu/eip/agriculture/). Links | BibTeX | Tags: @article{RN283,
title = {Biomass Estimation and Uncertainty Quantification From Tree Height},
author = {Qian Song and Conrad Albrecht and Zhitong Xiong and Xiao Zhu},
doi = {10.1109/JSTARS.2023.3271186},
year = {2023},
date = {2023-01-01},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
volume = {PP},
pages = {1-14},
note = {Manuscript received 24 February 2023; revised 19 April 2023; accepted 22 April 2023. Date of current version 5 June 2023. This work was jointly supported in part by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO—Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” under Grant 01DD20001 and in part by the German Federal Ministry for Economic Affairs and Climate Action in the framework of the “National Center of Excellence ML4Earth” under Grant 50EE2201C. (Corresponding author: Xiao Xiang Zhu.) Qian Song, Zhitong Xiong, and Xiao Xiang Zhu are with the Chair of Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), 85521 Munich, Germany (e-mail: qian.song@tum.de; zhitong.xiong@tum.de; xiaoxiang.zhu@tum.de). Conrad M. Albrecht is with the Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Munich, Germany (e-mail: Conrad. Albrecht@dlr.de).
Digital Object Identifier 10.1109/JSTARS.2023.3271186 1https://ec.europa.eu/eip/agriculture/},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Traoré, Kalifou René; Camero, Andrés; Zhu, Xiao Xiang A data-driven approach to neural architecture search initialization Journal Article Annals of Mathematics and Artificial Intelligence, 2023, ISSN: 1012-2443, (Authors acknowledge support by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat), by the Helmholtz Association through the Framework of Helmholtz AI[grant number: ZT-I-PF-5-01] - Local Unit “Munich Unit @Aeronautics, Space and Transport (MASTr)” and Helmholtz Excellent Professorship “Data Science in Earth Observation - Big Data Fusion for UrbanResearch”(W2-W3-100), by t he German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (Grant number: 01DD20001), the grant DeToL, and a DAAD Research fellowship. FundingOpen Access funding enabled and organized by Projekt DEAL. The funding is stated in Acknowledgment section. Data Availability https://github.com/kalifou/data-driven-initialization-to-search. Code Availability https://github.com/kalifou/data-driven-initialization-to-search.). Links | BibTeX | Tags: @article{RN284,
title = {A data-driven approach to neural architecture search initialization},
author = {Kalifou René Traoré and Andrés Camero and Xiao Xiang Zhu},
doi = {10.1007/s10472-022-09823-0},
issn = {1012-2443},
year = {2023},
date = {2023-01-01},
journal = {Annals of Mathematics and Artificial Intelligence},
note = {Authors acknowledge support by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat), by the Helmholtz Association through the Framework of Helmholtz AI[grant number: ZT-I-PF-5-01] - Local Unit “Munich Unit @Aeronautics, Space and Transport (MASTr)” and Helmholtz Excellent Professorship “Data Science in Earth Observation - Big Data Fusion for UrbanResearch”(W2-W3-100), by t he German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (Grant number: 01DD20001), the grant DeToL, and a DAAD Research fellowship. FundingOpen Access funding enabled and organized by Projekt DEAL. The funding is stated in Acknowledgment section. Data Availability https://github.com/kalifou/data-driven-initialization-to-search. Code Availability https://github.com/kalifou/data-driven-initialization-to-search.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Wei, Huang; Yilei, Shi; Zhitong, Xiong; Qi, Wang; Xiang, Zhu Xiao Semi-supervised bidirectional alignment for Remote Sensing cross-domain scene classification Journal Article ISPRS Journal of Photogrammetry and Remote Sensing, 195 , pp. 192-203, 2023, ISSN: 0924-2716, (The work is jointly supported by the German Research Foundation (DFG GZ: ZH 498/18-1; Project number: 519016653), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. [ERC-2016- StG-714087], Acronym: So2Sat), by the Helmholtz Association through the Framework of the Helmholtz Excellent Professorship ‘‘Data Science in Earth Observation - Big Data Fusion for Urban Research’’ (grant number: W2-W3-100), by the German Federal Ministry of Education
and Research (BMBF) in the framework of the international future AI lab ‘‘AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’’ (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ‘‘national center of excellence ML4Earth’’ (grant number: 50EE2201C).). Links | BibTeX | Tags: Bidirectional sample-class alignment, Cross-domain classification, Remote sensing, Semi-supervised domain adaptation @article{RN285,
title = {Semi-supervised bidirectional alignment for Remote Sensing cross-domain scene classification},
author = {Huang Wei and Shi Yilei and Xiong Zhitong and Wang Qi and Zhu Xiao Xiang},
url = {https://www.sciencedirect.com/science/article/pii/S0924271622003069},
doi = {https://doi.org/10.1016/j.isprsjprs.2022.11.013},
issn = {0924-2716},
year = {2023},
date = {2023-01-01},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {195},
pages = {192-203},
note = {The work is jointly supported by the German Research Foundation (DFG GZ: ZH 498/18-1; Project number: 519016653), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. [ERC-2016- StG-714087], Acronym: So2Sat), by the Helmholtz Association through the Framework of the Helmholtz Excellent Professorship ‘‘Data Science in Earth Observation - Big Data Fusion for Urban Research’’ (grant number: W2-W3-100), by the German Federal Ministry of Education
and Research (BMBF) in the framework of the international future AI lab ‘‘AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’’ (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ‘‘national center of excellence ML4Earth’’ (grant number: 50EE2201C).},
keywords = {Bidirectional sample-class alignment, Cross-domain classification, Remote sensing, Semi-supervised domain adaptation},
pubstate = {published},
tppubtype = {article}
}
|
| Xiangyu, Zhao; Jingliang, Hu; Lichao, Mou; Zhitong, Xiong; Xiang, Zhu Xiao Cross-city Landuse classification of remote sensing images via deep transfer learning Journal Article International Journal of Applied Earth Observation and Geoinformation, 122 , pp. 103358, 2023, ISSN: 1569-8432, (The work is jointly supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat), by the Helmholtz Association through the Framework of the Helmholtz Excellent Professorship ‘‘Data Science in Earth Observation - Big Data Fusion for Urban Research’’ (grant number: W2- W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ‘‘AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertain- ties, Ethics and Beyond’’ (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ‘‘national center of excellence ML4Earth’’ (grant number: 50EE2201C) and by the German Research Foundation(DFG GZ: ZH 498/18-1; Project number: 519016653).). Links | BibTeX | Tags: Cross-city classification, Deep learning, Domain adaptation, Local climate zone classification, Sentinel-1, Sentinel-2, Transfer learning @article{RN286,
title = {Cross-city Landuse classification of remote sensing images via deep transfer learning},
author = {Zhao Xiangyu and Hu Jingliang and Mou Lichao and Xiong Zhitong and Zhu Xiao Xiang},
url = {https://www.sciencedirect.com/science/article/pii/S1569843223001826},
doi = {https://doi.org/10.1016/j.jag.2023.103358},
issn = {1569-8432},
year = {2023},
date = {2023-01-01},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {122},
pages = {103358},
note = {The work is jointly supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat), by the Helmholtz Association through the Framework of the Helmholtz Excellent Professorship ‘‘Data Science in Earth Observation - Big Data Fusion for Urban Research’’ (grant number: W2- W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ‘‘AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertain- ties, Ethics and Beyond’’ (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ‘‘national center of excellence ML4Earth’’ (grant number: 50EE2201C) and by the German Research Foundation(DFG GZ: ZH 498/18-1; Project number: 519016653).},
keywords = {Cross-city classification, Deep learning, Domain adaptation, Local climate zone classification, Sentinel-1, Sentinel-2, Transfer learning},
pubstate = {published},
tppubtype = {article}
}
|
| Xin-Yi, Tong; Gui-Song, Xia; Xiang, Zhu Xiao Enabling country-scale land cover mapping with meter-resolution satellite imagery Journal Article ISPRS Journal of Photogrammetry and Remote Sensing, 196 , pp. 178-196, 2023, ISSN: 0924-2716, (The work is jointly supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and in- novation programme (grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat ), by the Helmholtz Association, Germany through the Framework of Helmholtz AI (grant number: ZT-I-PF-5-01) - Local Unit ‘‘Munich Unit @Aeronautics, Space and Transport (MASTr), Germany’’ and Helmholtz Excellent Professorship, Germany ‘‘Data Science in Earth Observation - Big Data Fusion for Urban Research’’(grant number: W2- W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ‘‘AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’’ (grant number: 01DD20001), by German Federal Ministry of Economics and Technology in the framework of the ‘‘national center of excellence ML4Earth’’ (grant number: 50EE2201C) and by the German Research Foundation (DFG GZ: ZH 498/18-1; Project number: 519016653).). Links | BibTeX | Tags: Classification, Dataset, Deep learning, Domain adaptation, Gaofen-1, Gaofen-2, High-spatial resolution, Land cover mapping, Megacity, PlanetScope, Sentinel-2, Transfer learning @article{RN287,
title = {Enabling country-scale land cover mapping with meter-resolution satellite imagery},
author = {Tong Xin-Yi and Xia Gui-Song and Zhu Xiao Xiang},
url = {https://www.sciencedirect.com/science/article/pii/S0924271622003264},
doi = {https://doi.org/10.1016/j.isprsjprs.2022.12.011},
issn = {0924-2716},
year = {2023},
date = {2023-01-01},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
volume = {196},
pages = {178-196},
note = {The work is jointly supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and in- novation programme (grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat ), by the Helmholtz Association, Germany through the Framework of Helmholtz AI (grant number: ZT-I-PF-5-01) - Local Unit ‘‘Munich Unit @Aeronautics, Space and Transport (MASTr), Germany’’ and Helmholtz Excellent Professorship, Germany ‘‘Data Science in Earth Observation - Big Data Fusion for Urban Research’’(grant number: W2- W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ‘‘AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond’’ (grant number: 01DD20001), by German Federal Ministry of Economics and Technology in the framework of the ‘‘national center of excellence ML4Earth’’ (grant number: 50EE2201C) and by the German Research Foundation (DFG GZ: ZH 498/18-1; Project number: 519016653).},
keywords = {Classification, Dataset, Deep learning, Domain adaptation, Gaofen-1, Gaofen-2, High-spatial resolution, Land cover mapping, Megacity, PlanetScope, Sentinel-2, Transfer learning},
pubstate = {published},
tppubtype = {article}
}
|
| Xiong, Zhitong; Huang, Wei; Hu, Jingtao; Zhu, Xiao THE Benchmark: Transferable Representation Learning for Monocular Height Estimation Journal Article IEEE Transactions on Geoscience and Remote Sensing, PP , pp. 1-1, 2023, (This work is jointly supported by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ”national center of excellence ML4Earth” (grant number: 50EE2201C), by the German Research Foundation (DFG GZ: ZH 498/18-1; Project number: 519016653), by the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) based on a resolution of the German Bundestag (grant number: 67KI32002B; Acronym: EKAPEx), and by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ”AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (grant number: 01DD20001). (Corresponding author: Xiao Xiang Zhu.) Z. Xiong, W. Huang, and X. X. Zhu are with the chair of Data Science in Earth Observation, Technical University of Munich (TUM), 80333 Munich, Germany. (e-mails: zhitong.xiong@tum.de; w2wei.huang@tum.de; xiaoxiang.zhu@tum.de) J. Hu is with the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, 710072 Xi’an, China (e-mails: jthu@mail.nwpu.edu.cn)). Links | BibTeX | Tags: @article{RN288,
title = {THE Benchmark: Transferable Representation Learning for Monocular Height Estimation},
author = {Zhitong Xiong and Wei Huang and Jingtao Hu and Xiao Zhu},
doi = {10.1109/TGRS.2023.3311764},
year = {2023},
date = {2023-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {PP},
pages = {1-1},
note = {This work is jointly supported by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ”national center of excellence ML4Earth” (grant number: 50EE2201C), by the German Research Foundation (DFG GZ: ZH 498/18-1; Project number: 519016653), by the German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV) based on a resolution of the German Bundestag (grant number: 67KI32002B; Acronym: EKAPEx), and by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ”AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (grant number: 01DD20001). (Corresponding author: Xiao Xiang Zhu.) Z. Xiong, W. Huang, and X. X. Zhu are with the chair of Data Science in Earth Observation, Technical University of Munich (TUM), 80333 Munich, Germany. (e-mails: zhitong.xiong@tum.de; w2wei.huang@tum.de; xiaoxiang.zhu@tum.de) J. Hu is with the School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, 710072 Xi’an, China (e-mails: jthu@mail.nwpu.edu.cn)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Xu, Fang; Shi, Yilei; Ebel, Patrick; Yang, Wen; Zhu, Xiao High-Resolution Cloud Removal with Multi-Modal and Multi-Resolution Data Fusion: A New Baseline and Benchmark Journal Article 2023, (The work of W. Yang is supported by the National Natural Science Foundation of China (NSFC) Regional Innovation and Development Joint Fund (No. U22A2010). The work of X. Zhu is jointly supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat), by the Helmholtz Association through the Framework of the Helmholtz Excellent Professorship “Data Science in Earth Observation - Big Data Fusion for Urban Research”(grant number: W2-W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab "AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond" (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the "national center of excellence ML4Earth" (grant number: 50EE2201C).). BibTeX | Tags: @article{RN289,
title = {High-Resolution Cloud Removal with Multi-Modal and Multi-Resolution Data Fusion: A New Baseline and Benchmark},
author = {Fang Xu and Yilei Shi and Patrick Ebel and Wen Yang and Xiao Zhu},
year = {2023},
date = {2023-01-01},
note = {The work of W. Yang is supported by the National Natural Science Foundation of China (NSFC) Regional Innovation and Development Joint Fund (No. U22A2010). The work of X. Zhu is jointly supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat), by the Helmholtz Association through the Framework of the Helmholtz Excellent Professorship “Data Science in Earth Observation - Big Data Fusion for Urban Research”(grant number: W2-W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab "AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond" (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the "national center of excellence ML4Earth" (grant number: 50EE2201C).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Xu, Qingsong; Shi, Yilei; Guo, Jianhua; Ouyang, Chaojun; Zhu, Xiao UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation Journal Article 2023, (The work is jointly supported by the German Research Foundation (DFG GZ: ZH 498/18-1; Project number: 519016653), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the “national center of excellence ML4Earth” (grant number: 50EE2201C) and by TUM in the framework of TUM Innovation Network “EarthCare: Twin Earth Methodologies for Biodiversity, Natural Hazards, and Urbanisation”. (Corresponding author: Xiao Xiang Zhu) Q. Xu, J. Guo and X. Zhu are with the Chair of Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany. (e-mails: qingsong.xu@tum.de; jianhua.guo@tum.de; xi- aoxiang.zhu@tum.de). Y. Shi is with the Chair of Remote Sensing Technology, Technical University of Munich (TUM), 80333 Munich, Germany. (e-mail: yilei.shi@tum.de). C. Ouyang is with the Key Laboratory of Mountain Hazards and Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China (e-mail: cjouyang@imde.ac.cn).). BibTeX | Tags: @article{RN290,
title = {UCDFormer: Unsupervised Change Detection Using a Transformer-driven Image Translation},
author = {Qingsong Xu and Yilei Shi and Jianhua Guo and Chaojun Ouyang and Xiao Zhu},
year = {2023},
date = {2023-01-01},
note = {The work is jointly supported by the German Research Foundation (DFG GZ: ZH 498/18-1; Project number: 519016653), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab “AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the “national center of excellence ML4Earth” (grant number: 50EE2201C) and by TUM in the framework of TUM Innovation Network “EarthCare: Twin Earth Methodologies for Biodiversity, Natural Hazards, and Urbanisation”. (Corresponding author: Xiao Xiang Zhu) Q. Xu, J. Guo and X. Zhu are with the Chair of Data Science in Earth Observation (SiPEO), Technical University of Munich (TUM), 80333 Munich, Germany. (e-mails: qingsong.xu@tum.de; jianhua.guo@tum.de; xi- aoxiang.zhu@tum.de). Y. Shi is with the Chair of Remote Sensing Technology, Technical University of Munich (TUM), 80333 Munich, Germany. (e-mail: yilei.shi@tum.de). C. Ouyang is with the Key Laboratory of Mountain Hazards and Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China (e-mail: cjouyang@imde.ac.cn).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Xu, Qingsong; Shi, Yilei; Yuan, Xin; Zhu, Xiao Xiang Universal Domain Adaptation for Remote Sensing Image Scene Classification Journal Article IEEE Transactions on Geoscience and Remote Sensing, 61 , pp. 1-15, 2023, (Funding Agency: 10.13039/501100001659-German Research Foundation (DFG GZ) (Grant Number: ZH 498/18-1 and 519016653) 10.13039/100010663-European Research Council (ERC) through the European Union’s Horizon 2020 Research and Innovation Programme (Grant Number: ERC-2016-StG-714087 (Acronym: So2Sat)) 10.13039/501100009318-Helmholtz Association under the Framework of the Helmholtz Excellent Professorship “Data Science in Earth Observation—Big Data Fusion for Urban Research” (Grant Number: W2-W3-100) 10.13039/501100002347-German Federal Ministry of Education and Research (BMBF) in the framework of the International Future AI Lab “AI4EO—Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (Grant Number: 01DD20001) 10.13039/501100006360-German Federal Ministry for Economic Affairs and Climate Action in the framework of the “National Center of Excellence ML4Earth” (Grant Number: 50EE2201C) 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62271414) 10.13039/501100004731-Zhejiang Provincial Natural Science Foundation of China (Grant Number: LR23F010001) Westlake Foundation (Grant Number: 2021B1 501-2) Research Center for Industries of the Future (RCIF) at Westlake University). Links | BibTeX | Tags: Remote sensing Adaptation models Data models Uncertainty Image classification Entropy Earth Remote sensing image classification source data generation (SDG) transferable weight universal domain adaptation (DA) @article{RN291,
title = {Universal Domain Adaptation for Remote Sensing Image Scene Classification},
author = {Qingsong Xu and Yilei Shi and Xin Yuan and Xiao Xiang Zhu},
doi = {10.1109/TGRS.2023.3235988},
year = {2023},
date = {2023-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {61},
pages = {1-15},
note = {Funding Agency: 10.13039/501100001659-German Research Foundation (DFG GZ) (Grant Number: ZH 498/18-1 and 519016653) 10.13039/100010663-European Research Council (ERC) through the European Union’s Horizon 2020 Research and Innovation Programme (Grant Number: ERC-2016-StG-714087 (Acronym: So2Sat)) 10.13039/501100009318-Helmholtz Association under the Framework of the Helmholtz Excellent Professorship “Data Science in Earth Observation—Big Data Fusion for Urban Research” (Grant Number: W2-W3-100) 10.13039/501100002347-German Federal Ministry of Education and Research (BMBF) in the framework of the International Future AI Lab “AI4EO—Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (Grant Number: 01DD20001) 10.13039/501100006360-German Federal Ministry for Economic Affairs and Climate Action in the framework of the “National Center of Excellence ML4Earth” (Grant Number: 50EE2201C) 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62271414) 10.13039/501100004731-Zhejiang Provincial Natural Science Foundation of China (Grant Number: LR23F010001) Westlake Foundation (Grant Number: 2021B1 501-2) Research Center for Industries of the Future (RCIF) at Westlake University},
keywords = {Remote sensing Adaptation models Data models Uncertainty Image classification Entropy Earth Remote sensing image classification source data generation (SDG) transferable weight universal domain adaptation (DA)},
pubstate = {published},
tppubtype = {article}
}
|
| Zhang, Fahong; Shi, Yilei; Xiong, Zhitong; Huang, Wei; Zhu, Xiao Pseudo Features-Guided Self-Training for Domain Adaptive Semantic Segmentation of Satellite Images Journal Article IEEE Transactions on Geoscience and Remote Sensing, PP , pp. 1-1, 2023, (The work is jointly supported by the German Research Foundation (DFG GZ: ZH 498/18-1; Project number: 519016653), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat), by the Helmholtz Association through the Framework of the Helmholtz Excellent Professorship “Data Science in Earth Observation - Big Data Fusion for Urban Research”(grant number: W2-W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ”AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ”national center of excellence ML4Earth” (grant number: 50EE2201C).
(Correspondence: Xiao Xiang Zhu) F. Zhang, Z. Xiong, W. Huang and X. Zhu are with the Chair of Data Science in Earth Observation, Technical University of Munich, 80333 Munich, Germany (e-mail: (fahong.zhang, zhitong.xiong, w2wei.huang, xiaoxiang.zhu)@tum.de). Y. Shi is with the Chair of Remote Sensing Technology, Technical University of Munich (TUM), 80333 Munich, Germany (e-mail: yilei.shi@tum.de)). Links | BibTeX | Tags: @article{RN292,
title = {Pseudo Features-Guided Self-Training for Domain Adaptive Semantic Segmentation of Satellite Images},
author = {Fahong Zhang and Yilei Shi and Zhitong Xiong and Wei Huang and Xiao Zhu},
doi = {10.1109/TGRS.2023.3281503},
year = {2023},
date = {2023-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {PP},
pages = {1-1},
note = {The work is jointly supported by the German Research Foundation (DFG GZ: ZH 498/18-1; Project number: 519016653), the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. [ERC-2016-StG-714087], Acronym: So2Sat), by the Helmholtz Association through the Framework of the Helmholtz Excellent Professorship “Data Science in Earth Observation - Big Data Fusion for Urban Research”(grant number: W2-W3-100), by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ”AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (grant number: 01DD20001) and by German Federal Ministry for Economic Affairs and Climate Action in the framework of the ”national center of excellence ML4Earth” (grant number: 50EE2201C).
(Correspondence: Xiao Xiang Zhu) F. Zhang, Z. Xiong, W. Huang and X. Zhu are with the Chair of Data Science in Earth Observation, Technical University of Munich, 80333 Munich, Germany (e-mail: (fahong.zhang, zhitong.xiong, w2wei.huang, xiaoxiang.zhu)@tum.de). Y. Shi is with the Chair of Remote Sensing Technology, Technical University of Munich (TUM), 80333 Munich, Germany (e-mail: yilei.shi@tum.de)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Zhou, Fei; Sun, Xin; Sun, Chengze; Dong, Junyu; Zhu, Xiao Xiang Adaptive Morphology Filter: A Lightweight Module for Deep Hyperspectral Image Classification Journal Article IEEE Transactions on Geoscience and Remote Sensing, 61 , pp. 1-16, 2023, (10.13039/501100001809-National Natural Science Foundation of China
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61971388)
10.13039/100005156-Alexander von Humboldt Foundation
German Federal Ministry of Education and Research (BMBF) through the Framework of the International Future AI Lab “AI4EO–Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond”
01DD20001
German Federal Ministry for Economic Affairs and Climate Action through the Framework of the “National Center of Excellence ML4Earth” (Grant Number: 50EE2201C)
Munich Center for Machine Learning). Links | BibTeX | Tags: Convolution Kernel Morphology Feature extraction Training Hyperspectral imaging Information filters Deep learning (DL) hyperspectral image (HSI) classification morphology filter structural reparameterization (SRP) @article{RN293,
title = {Adaptive Morphology Filter: A Lightweight Module for Deep Hyperspectral Image Classification},
author = {Fei Zhou and Xin Sun and Chengze Sun and Junyu Dong and Xiao Xiang Zhu},
doi = {10.1109/TGRS.2023.3327418},
year = {2023},
date = {2023-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = {61},
pages = {1-16},
note = {10.13039/501100001809-National Natural Science Foundation of China
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61971388)
10.13039/100005156-Alexander von Humboldt Foundation
German Federal Ministry of Education and Research (BMBF) through the Framework of the International Future AI Lab “AI4EO–Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond”
01DD20001
German Federal Ministry for Economic Affairs and Climate Action through the Framework of the “National Center of Excellence ML4Earth” (Grant Number: 50EE2201C)
Munich Center for Machine Learning},
keywords = {Convolution Kernel Morphology Feature extraction Training Hyperspectral imaging Information filters Deep learning (DL) hyperspectral image (HSI) classification morphology filter structural reparameterization (SRP)},
pubstate = {published},
tppubtype = {article}
}
|
| Dong, Runmin; Mou, Lichao; Chen, Mengxuan; Li, Weijia; Tong, Xin-Yi; Yuan, Shuai; Zhang, Lixian; Zheng, Juepeng; Zhu, Xiao Xiang; Fu, Haohuan Large-Scale Land Cover Mapping with Fine-Grained Classes via Class-Aware Semi-Supervised Semantic Segmentation Miscellaneous 2023, (This research was supported in part by the National Natural Science Foundation of China (Grant No. T2125006), Jiangsu Innovation Capacity Building Program (Project No. BM2022028), China Postdoctoral Science Foundation (Grant No. 2023M731871), the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ”AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (Grant No. 01DD20001), and Shuimu Tsinghua Scholar Project.). Links | BibTeX | Tags: @misc{RN37,
title = {Large-Scale Land Cover Mapping with Fine-Grained Classes via Class-Aware Semi-Supervised Semantic Segmentation},
author = {Runmin Dong and Lichao Mou and Mengxuan Chen and Weijia Li and Xin-Yi Tong and Shuai Yuan and Lixian Zhang and Juepeng Zheng and Xiao Xiang Zhu and Haohuan Fu},
url = {https://doi.org/10.1109/ICCV51070.2023.01539},
doi = {10.1109/ICCV51070.2023.01539},
year = {2023},
date = {2023-01-01},
pages = {16737-16747},
note = {This research was supported in part by the National Natural Science Foundation of China (Grant No. T2125006), Jiangsu Innovation Capacity Building Program (Project No. BM2022028), China Postdoctoral Science Foundation (Grant No. 2023M731871), the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ”AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (Grant No. 01DD20001), and Shuimu Tsinghua Scholar Project.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
|
| Ebel, Patrick; Garnot, Vivien Sainte Fare; Schmitt, Michael; Wegner, Jan; Zhu, Xiao UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series Book 2023, (This work is jointly supported by the Federal Ministry for Economic Affairs and Energy of Germany in the project “AI4Sentinels– Deep Learning for the Enrichment of Sentinel Satellite Imagery” (FKZ50EE1910), by the German Federal Ministry of Education and Research (BMBF) in the frame- work ”AI4EO – Artificial Intelligence for Earth Observation: Rea- soning, Uncertainties, Ethics and Beyond” (01DD20001) and by the German Federal Ministry of Economics and Technology in the framework of the ”national center of excellence ML4Earth” (50EE2201C).). Links | BibTeX | Tags: @book{RN38,
title = {UnCRtainTS: Uncertainty Quantification for Cloud Removal in Optical Satellite Time Series},
author = {Patrick Ebel and Vivien Sainte Fare Garnot and Michael Schmitt and Jan Wegner and Xiao Zhu},
doi = {10.1109/CVPRW59228.2023.00202},
year = {2023},
date = {2023-01-01},
pages = {2086-2096},
note = {This work is jointly supported by the Federal Ministry for Economic Affairs and Energy of Germany in the project “AI4Sentinels– Deep Learning for the Enrichment of Sentinel Satellite Imagery” (FKZ50EE1910), by the German Federal Ministry of Education and Research (BMBF) in the frame- work ”AI4EO – Artificial Intelligence for Earth Observation: Rea- soning, Uncertainties, Ethics and Beyond” (01DD20001) and by the German Federal Ministry of Economics and Technology in the framework of the ”national center of excellence ML4Earth” (50EE2201C).},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
|
2022
|
| Bamber J. L., Oppenheimer Kopp Aspinall M R E W P; Cooke, R M Climate Processes Driving the Uncertainty in Projections of Future Sea Level Rise: Findings From a Structured Expert Judgement Approach Journal Article Earth's Future, 10 (10), 2022. Abstract | Links | BibTeX | Tags: . @article{Bamber2022,
title = {Climate Processes Driving the Uncertainty in Projections of Future Sea Level Rise: Findings From a Structured Expert Judgement Approach},
author = {Bamber, J. L., Oppenheimer, M., Kopp, R. E., Aspinall, W. P., and Cooke, R. M. },
url = {https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2022EF002772},
doi = {2022EF002772},
year = {2022},
date = {2022-10-03},
journal = {Earth's Future},
volume = {10},
number = {10},
abstract = {The ice sheets covering Antarctica and Greenland present the greatest uncertainty in, and largest potential contribution to, future sea level rise. The uncertainty arises from a paucity of suitable observations covering the full range of ice sheet behaviors, incomplete understanding of the influences of diverse processes, and limitations in defining key boundary conditions for the numerical models. To investigate the impact of these uncertainties on ice sheet projections we undertook a structured expert judgement study. Here, we interrogate the findings of that study to identify the dominant drivers of uncertainty in projections and their relative importance as a function of ice sheet and time. We find that for the 21st century, Greenland surface melting, in particular the role of surface albedo effects, and West Antarctic ice dynamics, specifically the role of ice shelf buttressing, dominate the uncertainty. The importance of these effects holds under both a high-end 5°C global warming scenario and another that limits global warming to 2°C. During the 22nd century the dominant drivers of uncertainty shift. Under the 5°C scenario, East Antarctic ice dynamics dominate the uncertainty in projections, driven by the possible role of ice flow instabilities. These dynamic effects only become dominant, however, for a temperature scenario above the Paris Agreement 2°C target and beyond 2100. Our findings identify key processes and factors that need to be addressed in future modeling and observational studies in order to reduce uncertainties in ice sheet projections.},
keywords = {.},
pubstate = {published},
tppubtype = {article}
}
The ice sheets covering Antarctica and Greenland present the greatest uncertainty in, and largest potential contribution to, future sea level rise. The uncertainty arises from a paucity of suitable observations covering the full range of ice sheet behaviors, incomplete understanding of the influences of diverse processes, and limitations in defining key boundary conditions for the numerical models. To investigate the impact of these uncertainties on ice sheet projections we undertook a structured expert judgement study. Here, we interrogate the findings of that study to identify the dominant drivers of uncertainty in projections and their relative importance as a function of ice sheet and time. We find that for the 21st century, Greenland surface melting, in particular the role of surface albedo effects, and West Antarctic ice dynamics, specifically the role of ice shelf buttressing, dominate the uncertainty. The importance of these effects holds under both a high-end 5°C global warming scenario and another that limits global warming to 2°C. During the 22nd century the dominant drivers of uncertainty shift. Under the 5°C scenario, East Antarctic ice dynamics dominate the uncertainty in projections, driven by the possible role of ice flow instabilities. These dynamic effects only become dominant, however, for a temperature scenario above the Paris Agreement 2°C target and beyond 2100. Our findings identify key processes and factors that need to be addressed in future modeling and observational studies in order to reduce uncertainties in ice sheet projections. |
| Yann Ziegler Bramha Dutt Vishwakarma, Aoibheann Brady Stephen Chuter Sam Royston Richard Westaway Jonathan Bamber M L Can GPS and GRACE data be used to separate past and present-day surface loading in a data-driven approach? Journal Article Geophysical Journal International, 232 (2), pp. 884-901, 2022. Abstract | Links | BibTeX | Tags: . @article{Ziegler2022,
title = {Can GPS and GRACE data be used to separate past and present-day surface loading in a data-driven approach?},
author = {Yann Ziegler, Bramha Dutt Vishwakarma, Aoibheann Brady, Stephen Chuter, Sam Royston, Richard M Westaway, Jonathan L Bamber},
url = {https://doi.org/10.1093/gji/ggac365},
doi = {doi:10.1093/gji/ggac365},
year = {2022},
date = {2022-09-17},
journal = {Geophysical Journal International},
volume = {232},
number = {2},
pages = {884-901},
abstract = {Glacial isostatic adjustment (GIA) and the hydrological cycle are both associated with mass changes and vertical land motion (VLM), which are observed by GRACE and GPS, respectively. Hydrology-related VLM results from the instantaneous response of the elastic solid Earth to surface loading by freshwater, whereas GIA-related VLM reveals the long-term response of the viscoelastic Earth mantle to past ice loading history. Thus, observations of mass changes and VLM are interrelated, making GIA and hydrology difficult to quantify and study independently. In this work, we investigate the feasibility of separating these processes based on GRACE and GPS observations, in a fully data-driven and physically consistent approach. We take advantage of the differences in the spatio-temporal characteristics of the GIA and hydrology fields to estimate the respective contributions of each component using a Bayesian hierarchical modelling framework. A closed-loop synthetic test confirms that our method successfully solves this source separation problem. However, there are significant challenges when applying the same approach with actual observations and the answer to the main question of this study is more nuanced. In particular, in regions where GPS station coverage is sparse, the lack of informative data becomes a limiting factor.},
keywords = {.},
pubstate = {published},
tppubtype = {article}
}
Glacial isostatic adjustment (GIA) and the hydrological cycle are both associated with mass changes and vertical land motion (VLM), which are observed by GRACE and GPS, respectively. Hydrology-related VLM results from the instantaneous response of the elastic solid Earth to surface loading by freshwater, whereas GIA-related VLM reveals the long-term response of the viscoelastic Earth mantle to past ice loading history. Thus, observations of mass changes and VLM are interrelated, making GIA and hydrology difficult to quantify and study independently. In this work, we investigate the feasibility of separating these processes based on GRACE and GPS observations, in a fully data-driven and physically consistent approach. We take advantage of the differences in the spatio-temporal characteristics of the GIA and hydrology fields to estimate the respective contributions of each component using a Bayesian hierarchical modelling framework. A closed-loop synthetic test confirms that our method successfully solves this source separation problem. However, there are significant challenges when applying the same approach with actual observations and the answer to the main question of this study is more nuanced. In particular, in regions where GPS station coverage is sparse, the lack of informative data becomes a limiting factor. |
| Vishwakarma B. D., Ziegler Bamber Y J L; Royston, S Separating GIA signal from surface mass change using GPS and GRACE data Journal Article Geophysical Journal International, 232 (1), pp. 537-547, 2022. Abstract | Links | BibTeX | Tags: . @article{Vishwakarma2022,
title = {Separating GIA signal from surface mass change using GPS and GRACE data},
author = {Vishwakarma, B. D., Ziegler, Y., Bamber, J. L., and Royston, S.},
url = {https://doi.org/10.1093/gji/ggac336},
doi = {doi:10.1093/gji/ggac336},
year = {2022},
date = {2022-08-23},
journal = {Geophysical Journal International},
volume = {232},
number = {1},
pages = {537-547},
abstract = {The visco-elastic response of the solid Earth to the past glacial cycles and the present-day surface mass change (PDSMC) are detected by the geodetic observation systems such as global navigation satellite system and satellite gravimetry. Majority of the contemporary PDSMC is driven by climate change and in order to better understand them using the aforementioned geodetic observations, glacial isostatic adjustment (GIA) signal should be accounted first. The default approach is to use forward GIA models that use uncertain ice-load history and approximate Earth rheology to predict GIA, yielding large uncertainties. The proliferation of contemporary, global, geodetic observations and their coverage have therefore enabled estimation of data-driven GIA solutions. A novel framework is presented that uses geophysical relations between the vertical land motion (VLM) and geopotential anomaly due to GIA and PDSMC to express GPS VLM trends and GRACE geopotential trends as a function of either GIA or PDSMC, which can be easily solved using least-squares regression. The GIA estimates are data-driven and differ significantly from forward models over Alaska and Greenland.},
keywords = {.},
pubstate = {published},
tppubtype = {article}
}
The visco-elastic response of the solid Earth to the past glacial cycles and the present-day surface mass change (PDSMC) are detected by the geodetic observation systems such as global navigation satellite system and satellite gravimetry. Majority of the contemporary PDSMC is driven by climate change and in order to better understand them using the aforementioned geodetic observations, glacial isostatic adjustment (GIA) signal should be accounted first. The default approach is to use forward GIA models that use uncertain ice-load history and approximate Earth rheology to predict GIA, yielding large uncertainties. The proliferation of contemporary, global, geodetic observations and their coverage have therefore enabled estimation of data-driven GIA solutions. A novel framework is presented that uses geophysical relations between the vertical land motion (VLM) and geopotential anomaly due to GIA and PDSMC to express GPS VLM trends and GRACE geopotential trends as a function of either GIA or PDSMC, which can be easily solved using least-squares regression. The GIA estimates are data-driven and differ significantly from forward models over Alaska and Greenland. |
| Fang Xu Yilei Shi, Patrick Ebel Lei Yu Gui-Song Xia Wen Yang ; Zhu, Xiao Xiang GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion Journal Article Forthcoming ISPRS Journal of Photogrammetry and Remote Sensing, Forthcoming. BibTeX | Tags: . @article{xu2022,
title = {GLF-CR: SAR-Enhanced Cloud Removal with Global-Local Fusion},
author = {Fang Xu, Yilei Shi, Patrick Ebel,Lei Yu, Gui-Song Xia, Wen Yang, and Xiao Xiang Zhu},
year = {2022},
date = {2022-08-10},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
keywords = {.},
pubstate = {forthcoming},
tppubtype = {article}
}
|
| Mrinalini Kochupillai Matthias Kahl, Michael Schmitt Hannes Taubenböck Xiao Xiang Zhu Artificial Intelligence for Earth Observation: Understanding Emerging Ethical Issues and Opportunities Journal Article Forthcoming IEEE Geoscience and Remote Sensing Magazine, Forthcoming. BibTeX | Tags: . @article{kochupillai2022,
title = {Artificial Intelligence for Earth Observation: Understanding Emerging Ethical Issues and Opportunities},
author = {Mrinalini Kochupillai, Matthias Kahl, Michael Schmitt, Hannes Taubenböck, Xiao Xiang Zhu},
year = {2022},
date = {2022-08-10},
journal = {IEEE Geoscience and Remote Sensing Magazine},
keywords = {.},
pubstate = {forthcoming},
tppubtype = {article}
}
|
| Grinsted A., Bamber Bingham Buzzard Nias Ng J R S I K; Weeks, J The Transient Sea Level response to external forcing in CMIP6 models Journal Article Earth's Future, 10 (10), 2022. Abstract | Links | BibTeX | Tags: . @article{Grinsted2022,
title = {The Transient Sea Level response to external forcing in CMIP6 models},
author = {Grinsted, A., Bamber, J., Bingham, R., Buzzard, S., Nias, I., Ng, K., and Weeks, J.},
url = {https://doi.org/10.1029/2022EF002696},
doi = {2022EF002696},
year = {2022},
date = {2022-08-02},
journal = {Earth's Future},
volume = {10},
number = {10},
abstract = {Earth is warming and sea levels are rising as land-based ice is lost to melt, and oceans expand due to accumulation of heat. The pace of ice loss and steric expansion is linked to the intensity of warming. How much faster sea level will rise as climate warms is, however, highly uncertain and difficult to model. Here, we quantify the transient sea level sensitivity of the sea level budget in both models and observations. Models show little change in sensitivity to warming between the first and second half of the twenty-first century for most contributors. The exception is glaciers and ice caps (GIC) that have a greater sensitivity pre-2050 (2.8 ± 0.4 mm/yr/K) compared to later (0.7 ± 0.1 mm/yr/K). We attribute this change to the short response time of glaciers and their changing area over time. Model sensitivities of steric expansion (1.5 ± 0.2 mm/yr/K), and Greenland Ice Sheet mass loss (0.8 ± 0.2 mm/yr/K) are greater than, but still compatible with, corresponding estimates from historical data (1.4 ± 0.5 and 0.4 ± 0.2 mm/yr/K). Antarctic Ice Sheet (AIS) models tends to show lower rates of sea level rise (SLR) with warming (−0.0 ± 0.3 mm/yr/K) in contrast to historical estimates (0.4 ± 0.2 mm/yr/K). This apparent low bias in AIS sensitivity is only partly able to account for a similar low bias identified in the sensitivity of global mean sea level excluding GIC (3.1 ± 0.4 vs. 2.3 ± 0.4 mm/yr/K). The balance temperature, where SLR is zero, lies close to the pre-industrial value, implying that SLR can only be mitigated by substantial global cooling.},
keywords = {.},
pubstate = {published},
tppubtype = {article}
}
Earth is warming and sea levels are rising as land-based ice is lost to melt, and oceans expand due to accumulation of heat. The pace of ice loss and steric expansion is linked to the intensity of warming. How much faster sea level will rise as climate warms is, however, highly uncertain and difficult to model. Here, we quantify the transient sea level sensitivity of the sea level budget in both models and observations. Models show little change in sensitivity to warming between the first and second half of the twenty-first century for most contributors. The exception is glaciers and ice caps (GIC) that have a greater sensitivity pre-2050 (2.8 ± 0.4 mm/yr/K) compared to later (0.7 ± 0.1 mm/yr/K). We attribute this change to the short response time of glaciers and their changing area over time. Model sensitivities of steric expansion (1.5 ± 0.2 mm/yr/K), and Greenland Ice Sheet mass loss (0.8 ± 0.2 mm/yr/K) are greater than, but still compatible with, corresponding estimates from historical data (1.4 ± 0.5 and 0.4 ± 0.2 mm/yr/K). Antarctic Ice Sheet (AIS) models tends to show lower rates of sea level rise (SLR) with warming (−0.0 ± 0.3 mm/yr/K) in contrast to historical estimates (0.4 ± 0.2 mm/yr/K). This apparent low bias in AIS sensitivity is only partly able to account for a similar low bias identified in the sensitivity of global mean sea level excluding GIC (3.1 ± 0.4 vs. 2.3 ± 0.4 mm/yr/K). The balance temperature, where SLR is zero, lies close to the pre-industrial value, implying that SLR can only be mitigated by substantial global cooling. |
| Vishwakarma BD Ramsankaran R, Azam MF Bolch Mandal Srivastava Kumar Sahu Navinkumar PJ Tanniru SR Javed Soheb Dimri AP Yadav Devaraju Chinnasamy Reddy MJ Murugesan GP Arora Jain SK Ojha CSP Harrison T A S P R A M M B P M S; J, Bamber Challenges in Understanding the Variability of the Cryosphere in the Himalaya and Its Impact on Regional Water Resources Journal Article Frontiers in Water, 4 , 2022. Abstract | Links | BibTeX | Tags: . @article{BD2022,
title = {Challenges in Understanding the Variability of the Cryosphere in the Himalaya and Its Impact on Regional Water Resources},
author = {Vishwakarma BD, Ramsankaran R, Azam MF, Bolch T, Mandal A, Srivastava S, Kumar P, Sahu R, Navinkumar PJ, Tanniru SR, Javed A, Soheb M, Dimri AP, Yadav M, Devaraju B, Chinnasamy P, Reddy MJ, Murugesan GP, Arora M, Jain SK, Ojha CSP, Harrison S and Bamber J },
url = { https://doi.org/10.3389/frwa.2022.909246},
year = {2022},
date = {2022-07-28},
journal = {Frontiers in Water},
volume = {4},
abstract = {The Himalaya plays a vital role in regulating the freshwater availability for nearly a billion people living in the Indus, Ganga, and Brahmaputra River basins. Due to climate change and constantly evolving human-hydrosphere interactions, including land use/cover changes, groundwater extraction, reservoir or dam construction, water availability has undergone significant change, and is expected to change further in the future. Therefore, understanding the spatiotemporal evolution of the hydrological cycle over the Himalaya and its river basins has been one of the most critical exercises toward ensuring regional water security. However, due to the lack of extensive in-situ measurements, complex hydro-climatic environment, and limited collaborative efforts, large gaps in our understanding exist. Moreover, there are several significant issues with available studies, such as lack of consistent hydro-meteorological datasets, very few attempts at integrating different data types, limited spatiotemporal sampling of hydro-meteorological measurements, lack of open access to in-situ datasets, poorly accounted anthropogenic climate feedbacks, and limited understanding of the hydro-meteorological drivers over the region. These factors result in large uncertainties in our estimates of current and future water availability over the Himalaya, which constraints the development of sustainable water management strategies for its river catchments hampering our preparedness for the current and future changes in hydro-climate. To address these issues, a partnership development workshop entitled “Water sEcurity assessment in rIvers oriGinating from Himalaya (WEIGH),” was conducted between the 07th and 11th September 2020. Based on the intense discussions and deliberations among the participants, the most important and urgent research questions were identified. This white paper synthesizes the current understanding, highlights, and the most significant research gaps and research priorities for studying water availability in the Himalaya.},
keywords = {.},
pubstate = {published},
tppubtype = {article}
}
The Himalaya plays a vital role in regulating the freshwater availability for nearly a billion people living in the Indus, Ganga, and Brahmaputra River basins. Due to climate change and constantly evolving human-hydrosphere interactions, including land use/cover changes, groundwater extraction, reservoir or dam construction, water availability has undergone significant change, and is expected to change further in the future. Therefore, understanding the spatiotemporal evolution of the hydrological cycle over the Himalaya and its river basins has been one of the most critical exercises toward ensuring regional water security. However, due to the lack of extensive in-situ measurements, complex hydro-climatic environment, and limited collaborative efforts, large gaps in our understanding exist. Moreover, there are several significant issues with available studies, such as lack of consistent hydro-meteorological datasets, very few attempts at integrating different data types, limited spatiotemporal sampling of hydro-meteorological measurements, lack of open access to in-situ datasets, poorly accounted anthropogenic climate feedbacks, and limited understanding of the hydro-meteorological drivers over the region. These factors result in large uncertainties in our estimates of current and future water availability over the Himalaya, which constraints the development of sustainable water management strategies for its river catchments hampering our preparedness for the current and future changes in hydro-climate. To address these issues, a partnership development workshop entitled “Water sEcurity assessment in rIvers oriGinating from Himalaya (WEIGH),” was conducted between the 07th and 11th September 2020. Based on the intense discussions and deliberations among the participants, the most important and urgent research questions were identified. This white paper synthesizes the current understanding, highlights, and the most significant research gaps and research priorities for studying water availability in the Himalaya. |
| Sam Royston Rory J. Bingham, ; Bamber, Jonathan L Attributing decadal climate variability in coastal sea-level trends Journal Article Ocean Science, 18 (4), pp. 1093–1107, 2022. Abstract | Links | BibTeX | Tags: . @article{Royston2022,
title = {Attributing decadal climate variability in coastal sea-level trends},
author = { Sam Royston, Rory J. Bingham, and Jonathan L. Bamber },
url = {https://doi.org/10.5194/os-18-1093-2022},
year = {2022},
date = {2022-07-27},
journal = {Ocean Science},
volume = {18},
number = {4},
pages = {1093–1107},
abstract = {Decadal sea-level variability masks longer-term changes due to natural and anthropogenic drivers in short-duration records and increases uncertainty in trend and acceleration estimates. When making regional coastal management and adaptation decisions, it is important to understand the drivers of these changes to account for periods of reduced or enhanced sea-level change. The variance in decadal sea-level trends about the global mean is quantified and mapped around the global coastlines of the Atlantic, Pacific, and Indian oceans from historical CMIP6 runs and a high-resolution ocean model forced by reanalysis data. We reconstruct coastal, sea-level trends via linear relationships with climate mode and oceanographic indices. Using this approach, more than one-third of the variability in decadal sea-level trends can be explained by climate indices at 24.6 % to 73.1 % of grid cells located within 25 km of a coast in the Atlantic, Pacific, and Indian oceans. At 10.9 % of the world's coastline, climate variability explains over two-thirds of the decadal sea-level trend. By investigating the steric, manometric, and gravitational components of sea-level trend independently, it is apparent that much of the coastal ocean variability is dominated by the manometric signal, the consequence of the open-ocean steric signal propagating onto the continental shelf. Additionally, decadal variability in the gravitational, rotational, and solid-Earth deformation (GRD) signal should not be ignored in the total. There are locations such as the Persian Gulf and African west coast where decadal sea-level variability is historically small that are susceptible to future changes in hydrology and/or ice mass changes that drive intensified regional GRD sea-level change above the global mean. The magnitude of variance explainable by climate modes quantified in this study indicates an enhanced uncertainty in projections of short- to mid-term regional sea-level trend.},
keywords = {.},
pubstate = {published},
tppubtype = {article}
}
Decadal sea-level variability masks longer-term changes due to natural and anthropogenic drivers in short-duration records and increases uncertainty in trend and acceleration estimates. When making regional coastal management and adaptation decisions, it is important to understand the drivers of these changes to account for periods of reduced or enhanced sea-level change. The variance in decadal sea-level trends about the global mean is quantified and mapped around the global coastlines of the Atlantic, Pacific, and Indian oceans from historical CMIP6 runs and a high-resolution ocean model forced by reanalysis data. We reconstruct coastal, sea-level trends via linear relationships with climate mode and oceanographic indices. Using this approach, more than one-third of the variability in decadal sea-level trends can be explained by climate indices at 24.6 % to 73.1 % of grid cells located within 25 km of a coast in the Atlantic, Pacific, and Indian oceans. At 10.9 % of the world's coastline, climate variability explains over two-thirds of the decadal sea-level trend. By investigating the steric, manometric, and gravitational components of sea-level trend independently, it is apparent that much of the coastal ocean variability is dominated by the manometric signal, the consequence of the open-ocean steric signal propagating onto the continental shelf. Additionally, decadal variability in the gravitational, rotational, and solid-Earth deformation (GRD) signal should not be ignored in the total. There are locations such as the Persian Gulf and African west coast where decadal sea-level variability is historically small that are susceptible to future changes in hydrology and/or ice mass changes that drive intensified regional GRD sea-level change above the global mean. The magnitude of variance explainable by climate modes quantified in this study indicates an enhanced uncertainty in projections of short- to mid-term regional sea-level trend. |
| van den Khan S. A., Colgan Neumann Broeke Brunt Noël Bamber Hassan W T A M R K M B J L J; Bjørk, A A Accelerating Ice Loss From Peripheral Glaciers in North Greenland Journal Article Geophysical Research Letters, 49 (12), 2022. Abstract | Links | BibTeX | Tags: . @article{Khan2022c,
title = {Accelerating Ice Loss From Peripheral Glaciers in North Greenland},
author = {Khan, S. A., Colgan, W., Neumann, T. A., van den Broeke, M. R., Brunt, K. M., Noël, B., Bamber, J. L., Hassan, J., and Bjørk, A. A. },
url = {https://doi.org/10.1029/2022GL098915},
doi = {2022GL098915},
year = {2022},
date = {2022-06-16},
journal = {Geophysical Research Letters},
volume = {49},
number = {12},
abstract = {In recent decades, Greenland's peripheral glaciers have experienced large-scale mass loss, resulting in a substantial contribution to sea level rise. While their total area of Greenland ice cover is relatively small (4%), their mass loss is disproportionally large compared to the Greenland ice sheet. Satellite altimetry from Ice, Cloud, and land Elevation Satellite (ICESat) and ICESat-2 shows that mass loss from Greenland's peripheral glaciers increased from 27.2 ± 6.2 Gt/yr (February 2003–October 2009) to 42.3 ± 6.2 Gt/yr (October 2018–December 2021). These relatively small glaciers now constitute 11 ± 2% of Greenland's ice loss and contribute to global sea level rise. In the period October 2018–December 2021, mass loss increased by a factor of four for peripheral glaciers in North Greenland. While peripheral glacier mass loss is widespread, we also observe a complex regional pattern where increases in precipitation at high altitudes have partially counteracted increases in melt at low altitude.},
keywords = {.},
pubstate = {published},
tppubtype = {article}
}
In recent decades, Greenland's peripheral glaciers have experienced large-scale mass loss, resulting in a substantial contribution to sea level rise. While their total area of Greenland ice cover is relatively small (4%), their mass loss is disproportionally large compared to the Greenland ice sheet. Satellite altimetry from Ice, Cloud, and land Elevation Satellite (ICESat) and ICESat-2 shows that mass loss from Greenland's peripheral glaciers increased from 27.2 ± 6.2 Gt/yr (February 2003–October 2009) to 42.3 ± 6.2 Gt/yr (October 2018–December 2021). These relatively small glaciers now constitute 11 ± 2% of Greenland's ice loss and contribute to global sea level rise. In the period October 2018–December 2021, mass loss increased by a factor of four for peripheral glaciers in North Greenland. While peripheral glacier mass loss is widespread, we also observe a complex regional pattern where increases in precipitation at high altitudes have partially counteracted increases in melt at low altitude. |
| Rosa, Laura Elena Cué La; Oliveira, Dário Augusto Borges Learning from Label Proportions with Prototypical Contrastive Clustering Journal Article Proceedings of the AAAI Conference on Artificial Intelligence, 36 (2), pp. 2153-2161, 2022. Links | BibTeX | Tags: @article{Rosa_Oliveira_2022,
title = {Learning from Label Proportions with Prototypical Contrastive Clustering},
author = {Laura Elena Cué La Rosa and Dário Augusto Borges Oliveira},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/20112},
doi = {10.1609/aaai.v36i2.20112},
year = {2022},
date = {2022-06-01},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {36},
number = {2},
pages = {2153-2161},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Chuter S. J., Zammit-Mangion Rougier Dawson A J G; Bamber, J L Mass evolution of the Antarctic Peninsula over the last 2 decades from a joint Bayesian inversion Journal Article The Cryosphere, 16 (4), pp. 1349-1367, 2022. Abstract | Links | BibTeX | Tags: . @article{Chuter2022,
title = {Mass evolution of the Antarctic Peninsula over the last 2 decades from a joint Bayesian inversion},
author = {Chuter, S. J., Zammit-Mangion, A., Rougier, J., Dawson, G., and Bamber, J. L. },
url = {https://tc.copernicus.org/articles/16/1349/2022/},
doi = {tc-16-1349-2022},
year = {2022},
date = {2022-04-12},
journal = {The Cryosphere},
volume = {16},
number = {4},
pages = {1349-1367},
abstract = {The Antarctic Peninsula has become an increasingly important component of the Antarctic Ice Sheet mass budget over the last 2 decades, with mass losses generally increasing. However, due to the challenges presented by the topography and geometry of the region, there remain large variations in mass balance estimates from conventional approaches and in assessing the relative contribution of individual ice sheet processes. Here, we use a regionally optimized Bayesian hierarchical model joint inversion approach that combines data from multiple altimetry studies (ENVISAT, ICESat, CryoSat-2 swath), gravimetry (GRACE and GRACE-FO), and localized DEM differencing observations to solve for annual mass trends and their attribution to individual driving processes for the period 2003–2019. This is first time that such localized observations have been assimilated directly to estimate mass balance as part of a wider-scale regional assessment. The region experienced a mass imbalance rate of Gt yr−1 between 2003 and 2019, predominantly driven by accelerations in ice dynamic mass losses in the first decade and sustained thereafter. Inter-annual variability is driven by surface processes, particularly in 2016 due to increased precipitation driven by an extreme El Niño, which temporarily returned the sector back to a state of positive mass balance. In the West Palmer Land and the English Coast regions, surface processes are a greater contributor to mass loss than ice dynamics in the early part of the 2010s. Our results show good agreement with conventional and other combination approaches, improving confidence in the robustness of mass trend estimates, and in turn, understanding of the region's response to changes in external forcing.},
keywords = {.},
pubstate = {published},
tppubtype = {article}
}
The Antarctic Peninsula has become an increasingly important component of the Antarctic Ice Sheet mass budget over the last 2 decades, with mass losses generally increasing. However, due to the challenges presented by the topography and geometry of the region, there remain large variations in mass balance estimates from conventional approaches and in assessing the relative contribution of individual ice sheet processes. Here, we use a regionally optimized Bayesian hierarchical model joint inversion approach that combines data from multiple altimetry studies (ENVISAT, ICESat, CryoSat-2 swath), gravimetry (GRACE and GRACE-FO), and localized DEM differencing observations to solve for annual mass trends and their attribution to individual driving processes for the period 2003–2019. This is first time that such localized observations have been assimilated directly to estimate mass balance as part of a wider-scale regional assessment. The region experienced a mass imbalance rate of Gt yr−1 between 2003 and 2019, predominantly driven by accelerations in ice dynamic mass losses in the first decade and sustained thereafter. Inter-annual variability is driven by surface processes, particularly in 2016 due to increased precipitation driven by an extreme El Niño, which temporarily returned the sector back to a state of positive mass balance. In the West Palmer Land and the English Coast regions, surface processes are a greater contributor to mass loss than ice dynamics in the early part of the 2010s. Our results show good agreement with conventional and other combination approaches, improving confidence in the robustness of mass trend estimates, and in turn, understanding of the region's response to changes in external forcing. |
| Qian, Kun; Wang, Yuanyuan; Shi, Yilei; Zhu, Xiao Xiang γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning Journal Article Forthcoming IEEE Transactions on Geoscience and Remote Sensing, Forthcoming, (in press). Abstract | BibTeX | Tags: @article{,
title = {γ-Net: Superresolving SAR Tomographic Inversion via Deep Learning},
author = {Kun Qian and Yuanyuan Wang and Yilei Shi and Xiao Xiang Zhu},
year = {2022},
date = {2022-03-30},
booktitle = {IEEE Transactions on Geoscience and Remote Sensing},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
abstract = {(This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.) Synthetic aperture radar tomography (TomoSAR) has been extensively employed in 3-D reconstruction in dense urban areas using high-resolution SAR acquisitions. Compressive sensing (CS)-based algorithms are generally considered as the state of the art in super-resolving TomoSAR, in particular in the single look case. This superior performance comes at the cost of extra computational burdens, because of the sparse reconstruction, which cannot be solved analytically and we need to employ computationally expensive iterative solvers. In this paper, we propose a novel deep learning-based super-resolving TomoSAR inversion approach, γ-Net, to tackle this challenge. γ-Net adopts advanced complex-valued learned iterative shrinkage thresholding algorithm (CV-LISTA) to mimic the iterative optimization step in sparse reconstruction. Simulations show the height estimate from a well-trained γ-Net approaches the Cramér-Rao lower bound while improving the computational efficiency by 1 to 2 orders of magnitude comparing to the first-order CS-based methods. It also shows no degradation in the super-resolution power comparing to the state-of-the-art second-order TomoSAR solvers, which are much more computationally expensive than the first-order methods. Specifically, γ-Net reaches more than 90% detection rate in moderate super-resolving cases at 25 measurements at 6dB SNR. Moreover, simulation at limited baselines demonstrates that the proposed algorithm outperforms the second-order CS-based method by a fair margin. Test on real TerraSAR-X data with just 6 interferograms also shows high-quality 3-D reconstruction with high-density detected double scatterers.},
note = {in press},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
(This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.) Synthetic aperture radar tomography (TomoSAR) has been extensively employed in 3-D reconstruction in dense urban areas using high-resolution SAR acquisitions. Compressive sensing (CS)-based algorithms are generally considered as the state of the art in super-resolving TomoSAR, in particular in the single look case. This superior performance comes at the cost of extra computational burdens, because of the sparse reconstruction, which cannot be solved analytically and we need to employ computationally expensive iterative solvers. In this paper, we propose a novel deep learning-based super-resolving TomoSAR inversion approach, γ-Net, to tackle this challenge. γ-Net adopts advanced complex-valued learned iterative shrinkage thresholding algorithm (CV-LISTA) to mimic the iterative optimization step in sparse reconstruction. Simulations show the height estimate from a well-trained γ-Net approaches the Cramér-Rao lower bound while improving the computational efficiency by 1 to 2 orders of magnitude comparing to the first-order CS-based methods. It also shows no degradation in the super-resolution power comparing to the state-of-the-art second-order TomoSAR solvers, which are much more computationally expensive than the first-order methods. Specifically, γ-Net reaches more than 90% detection rate in moderate super-resolving cases at 25 measurements at 6dB SNR. Moreover, simulation at limited baselines demonstrates that the proposed algorithm outperforms the second-order CS-based method by a fair margin. Test on real TerraSAR-X data with just 6 interferograms also shows high-quality 3-D reconstruction with high-density detected double scatterers. |
| van den Shfaqat A. Khan Jonathan L. Bamber, Eric Rignot Veit Helm Andy Aschwanden David Holland Michiel Broeke Michalea King Brice Noël Martin Truffer Angelika Humbert William Colgan Saurabh Vijay Peter Kuipers Munneke M Greenland Mass Trends From Airborne and Satellite Altimetry During 2011–2020 Journal Article Journal of Geophysical Research: Earth Surface, 127 (4), 2022. Abstract | Links | BibTeX | Tags: . @article{Khan2022b,
title = {Greenland Mass Trends From Airborne and Satellite Altimetry During 2011–2020},
author = {Shfaqat A. Khan, Jonathan L. Bamber, Eric Rignot, Veit Helm, Andy Aschwanden, David M. Holland, Michiel van den Broeke, Michalea King, Brice Noël, Martin Truffer, Angelika Humbert, William Colgan, Saurabh Vijay, Peter Kuipers Munneke },
url = {https://doi.org/10.1029/2021JF006505},
doi = {e2021JF006505},
year = {2022},
date = {2022-03-21},
journal = {Journal of Geophysical Research: Earth Surface},
volume = {127},
number = {4},
abstract = {We use satellite and airborne altimetry to estimate annual mass changes of the Greenland Ice Sheet. We estimate ice loss corresponding to a sea-level rise of 6.9 ± 0.4 mm from April 2011 to April 2020, with a highest annual ice loss rate of 1.4 mm/yr sea-level equivalent from April 2019 to April 2020. On a regional scale, our annual mass loss timeseries reveals 10–15 m/yr dynamic thickening at the terminus of Jakobshavn Isbræ from April 2016 to April 2018, followed by a return to dynamic thinning. We observe contrasting patterns of mass loss acceleration in different basins across the ice sheet and suggest that these spatiotemporal trends could be useful for calibrating and validating prognostic ice sheet models. In addition to resolving the spatial and temporal fingerprint of Greenland's recent ice loss, these mass loss grids are key for partitioning contemporary elastic vertical land motion from longer-term glacial isostatic adjustment (GIA) trends at GPS stations around the ice sheet. Our ice-loss product results in a significantly different GIA interpretation from a previous ice-loss product.},
keywords = {.},
pubstate = {published},
tppubtype = {article}
}
We use satellite and airborne altimetry to estimate annual mass changes of the Greenland Ice Sheet. We estimate ice loss corresponding to a sea-level rise of 6.9 ± 0.4 mm from April 2011 to April 2020, with a highest annual ice loss rate of 1.4 mm/yr sea-level equivalent from April 2019 to April 2020. On a regional scale, our annual mass loss timeseries reveals 10–15 m/yr dynamic thickening at the terminus of Jakobshavn Isbræ from April 2016 to April 2018, followed by a return to dynamic thinning. We observe contrasting patterns of mass loss acceleration in different basins across the ice sheet and suggest that these spatiotemporal trends could be useful for calibrating and validating prognostic ice sheet models. In addition to resolving the spatial and temporal fingerprint of Greenland's recent ice loss, these mass loss grids are key for partitioning contemporary elastic vertical land motion from longer-term glacial isostatic adjustment (GIA) trends at GPS stations around the ice sheet. Our ice-loss product results in a significantly different GIA interpretation from a previous ice-loss product. |
| Tom Mitcham G. Hilmar Gudmundsson, ; Bamber, Jonathan L The instantaneous impact of calving and thinning on the Larsen C Ice Shelf Journal Article The Cryosphere, 16 (3), pp. 883–901, 2022. Abstract | Links | BibTeX | Tags: . @article{Mitcham2022,
title = {The instantaneous impact of calving and thinning on the Larsen C Ice Shelf},
author = {Tom Mitcham, G. Hilmar Gudmundsson, and Jonathan L. Bamber },
url = {https://doi.org/10.5194/tc-16-883-2022},
doi = {tc-16-883-2022},
year = {2022},
date = {2022-03-11},
journal = {The Cryosphere},
volume = {16},
number = {3},
pages = {883–901},
abstract = {The Antarctic Peninsula has seen rapid and widespread changes in the extent of its ice shelves in recent decades, including the collapse of the Larsen A and B ice shelves in 1995 and 2002, respectively. In 2017 the Larsen C Ice Shelf (LCIS) lost around 10 % of its area by calving one of the largest icebergs ever recorded (A68). This has raised questions about the structural integrity of the shelf and the impact of any changes in its extent on the flow of its tributary glaciers. In this work, we used an ice flow model to study the instantaneous impact of changes in the thickness and extent of the LCIS on ice dynamics and in particular on changes in the grounding line flux (GLF). We initialised the model to a pre-A68 calving state and first replicated the calving of the A68 iceberg. We found that there was a limited instantaneous impact on upstream flow – with speeds increasing by less than 10 % across almost all of the shelf – and a 0.28 % increase in GLF. This result is supported by observations of ice velocity made before and after the calving event. We then perturbed the ice-shelf geometry through a series of instantaneous, idealised calving and thinning experiments of increasing magnitude. We found that significant changes to the geometry of the ice shelf, through both calving and thinning, resulted in limited instantaneous changes in GLF. For example, to produce a doubling of GLF from calving, the new calving front needed to be moved to 5 km from the grounding line, removing almost the entire ice shelf. For thinning, over 200 m of the ice-shelf thickness had to be removed across the whole shelf to produce a doubling of GLF. Calculating the instantaneous increase in GLF (607 %) after removing the entire ice shelf allowed us to quantify the total amount of buttressing provided by the LCIS. From this, we identified that the region of the ice shelf in the first 5 km downstream of the grounding line provided over 80 % of the buttressing capacity of the shelf. This is due to the large resistive stresses generated in the narrow, local embayments downstream of the largest tributary glaciers.},
keywords = {.},
pubstate = {published},
tppubtype = {article}
}
The Antarctic Peninsula has seen rapid and widespread changes in the extent of its ice shelves in recent decades, including the collapse of the Larsen A and B ice shelves in 1995 and 2002, respectively. In 2017 the Larsen C Ice Shelf (LCIS) lost around 10 % of its area by calving one of the largest icebergs ever recorded (A68). This has raised questions about the structural integrity of the shelf and the impact of any changes in its extent on the flow of its tributary glaciers. In this work, we used an ice flow model to study the instantaneous impact of changes in the thickness and extent of the LCIS on ice dynamics and in particular on changes in the grounding line flux (GLF). We initialised the model to a pre-A68 calving state and first replicated the calving of the A68 iceberg. We found that there was a limited instantaneous impact on upstream flow – with speeds increasing by less than 10 % across almost all of the shelf – and a 0.28 % increase in GLF. This result is supported by observations of ice velocity made before and after the calving event. We then perturbed the ice-shelf geometry through a series of instantaneous, idealised calving and thinning experiments of increasing magnitude. We found that significant changes to the geometry of the ice shelf, through both calving and thinning, resulted in limited instantaneous changes in GLF. For example, to produce a doubling of GLF from calving, the new calving front needed to be moved to 5 km from the grounding line, removing almost the entire ice shelf. For thinning, over 200 m of the ice-shelf thickness had to be removed across the whole shelf to produce a doubling of GLF. Calculating the instantaneous increase in GLF (607 %) after removing the entire ice shelf allowed us to quantify the total amount of buttressing provided by the LCIS. From this, we identified that the region of the ice shelf in the first 5 km downstream of the grounding line provided over 80 % of the buttressing capacity of the shelf. This is due to the large resistive stresses generated in the narrow, local embayments downstream of the largest tributary glaciers. |
| Li T., Dawson Chuter G J S J; Bamber, J L A high-resolution Antarctic grounding zone product from ICESat-2 laser altimetry Journal Article Earth System Science Data, 14 (2), pp. 535–557, 2022. Abstract | Links | BibTeX | Tags: . @article{Li2022c,
title = {A high-resolution Antarctic grounding zone product from ICESat-2 laser altimetry},
author = {Li, T., Dawson, G. J., Chuter, S. J., and Bamber, J. L. },
url = {https://doi.org/10.5194/essd-14-535-2022},
doi = {essd-14-535-2022},
year = {2022},
date = {2022-02-08},
journal = {Earth System Science Data},
volume = {14},
number = {2},
pages = {535–557},
abstract = {The Antarctic grounding zone, which is the transition between the fully grounded ice sheet to freely floating ice shelf, plays a critical role in ice sheet stability, mass budget calculations, and ice sheet model projections. It is therefore important to continuously monitor its location and migration over time. Here we present the first ICESat-2-derived high-resolution grounding zone product of the Antarctic Ice Sheet, including three important boundaries: the inland limit of tidal flexure (Point F), inshore limit of hydrostatic equilibrium (Point H), and the break in slope (Point Ib). This dataset was derived from automated techniques developed in this study, using ICESat-2 laser altimetry repeat tracks between 30 March 2019 and 30 September 2020. The new grounding zone product has a near-complete coverage of the Antarctic Ice Sheet with a total of 21 346 Point F, 18 149 Point H, and 36 765 Point Ib locations identified, including the difficult-to-survey grounding zones, such as the fast-flowing glaciers draining into the Amundsen Sea embayment. The locations of newly derived ICESat-2 landward limit of tidal flexure agree well with the most recent differential synthetic aperture radar interferometry (DInSAR) observations in 2018, with a mean absolute separation and standard deviation of 0.02 and 0.02 km, respectively. By comparing the ICESat-2-derived grounding zone with the previous grounding zone products, we find a grounding line retreat of up to 15 km on the Crary Ice Rise of Ross Ice Shelf and a pervasive landward grounding line migration along the Amundsen Sea embayment during the past 2 decades. We also identify the presence of ice plains on the Filchner–Ronne Ice Shelf and the influence of oscillating ocean tides on grounding zone migration. The product derived from this study is available at https://doi.org/10.5523/bris.bnqqyngt89eo26qk8keckglww (Li et al., 2021) and is archived and maintained at the National Snow and Ice Data Center.},
keywords = {.},
pubstate = {published},
tppubtype = {article}
}
The Antarctic grounding zone, which is the transition between the fully grounded ice sheet to freely floating ice shelf, plays a critical role in ice sheet stability, mass budget calculations, and ice sheet model projections. It is therefore important to continuously monitor its location and migration over time. Here we present the first ICESat-2-derived high-resolution grounding zone product of the Antarctic Ice Sheet, including three important boundaries: the inland limit of tidal flexure (Point F), inshore limit of hydrostatic equilibrium (Point H), and the break in slope (Point Ib). This dataset was derived from automated techniques developed in this study, using ICESat-2 laser altimetry repeat tracks between 30 March 2019 and 30 September 2020. The new grounding zone product has a near-complete coverage of the Antarctic Ice Sheet with a total of 21 346 Point F, 18 149 Point H, and 36 765 Point Ib locations identified, including the difficult-to-survey grounding zones, such as the fast-flowing glaciers draining into the Amundsen Sea embayment. The locations of newly derived ICESat-2 landward limit of tidal flexure agree well with the most recent differential synthetic aperture radar interferometry (DInSAR) observations in 2018, with a mean absolute separation and standard deviation of 0.02 and 0.02 km, respectively. By comparing the ICESat-2-derived grounding zone with the previous grounding zone products, we find a grounding line retreat of up to 15 km on the Crary Ice Rise of Ross Ice Shelf and a pervasive landward grounding line migration along the Amundsen Sea embayment during the past 2 decades. We also identify the presence of ice plains on the Filchner–Ronne Ice Shelf and the influence of oscillating ocean tides on grounding zone migration. The product derived from this study is available at https://doi.org/10.5523/bris.bnqqyngt89eo26qk8keckglww (Li et al., 2021) and is archived and maintained at the National Snow and Ice Data Center. |
| Li, Tian; Dawson, Geoffrey J; Chuter, Stephen J; Bamber, Jonathan L A high-resolution Antarctic grounding zone product from ICESat-2 laser altimetry Journal Article Earth System Science Data, 14 , pp. 535-557, 2022, ISSN: 1866-3516. Abstract | Links | BibTeX | Tags: @article{Li2022,
title = {A high-resolution Antarctic grounding zone product from ICESat-2 laser altimetry},
author = {Tian Li and Geoffrey J Dawson and Stephen J Chuter and Jonathan L Bamber},
url = {https://essd.copernicus.org/articles/14/535/2022/},
doi = {10.5194/essd-14-535-2022},
issn = {1866-3516},
year = {2022},
date = {2022-01-01},
journal = {Earth System Science Data},
volume = {14},
pages = {535-557},
abstract = { },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
<p><![CDATA[Abstract. The Antarctic grounding zone, which is the transition between the fully grounded ice sheet to freely floating ice shelf, plays a critical role in ice sheet stability, mass budget calculations, and ice sheet model projections. It is therefore important to continuously monitor its location and migration over time. Here we present the first ICESat-2-derived high-resolution grounding zone product of the Antarctic Ice Sheet, including three important boundaries: the inland limit of tidal flexure (Point F), inshore limit of hydrostatic equilibrium (Point H), and the break in slope (Point Ib). This dataset was derived from automated techniques developed in this study, using ICESat-2 laser altimetry repeat tracks between 30 March 2019 and 30 September 2020. The new grounding zone product has a near-complete coverage of the Antarctic Ice Sheet with a total of 21 346 Point F, 18 149 Point H, and 36 765 Point Ib locations identified, including the difficult-to-survey grounding zones, such as the fast-flowing glaciers draining into the Amundsen Sea embayment. The locations of newly derived ICESat-2 landward limit of tidal flexure agree well with the most recent differential synthetic aperture radar interferometry (DInSAR) observations in 2018, with a mean absolute separation and standard deviation of 0.02 and 0.02 km, respectively. By comparing the ICESat-2-derived grounding zone with the previous grounding zone products, we find a grounding line retreat of up to 15 km on the Crary Ice Rise of Ross Ice Shelf and a pervasive landward grounding line migration along the Amundsen Sea embayment during the past 2 decades. We also identify the presence of ice plains on the Filchner–Ronne Ice Shelf and the influence of oscillating ocean tides on grounding zone migration. The product derived from this study is available at https://doi.org/10.5523/bris.bnqqyngt89eo26qk8keckglww (Li et al., 2021) and is archived and maintained at the National Snow and Ice Data Center.]]></p> |
| Lehmann, Fanny; Vishwakarma, Bramha Dutt; Bamber, Jonathan How well are we able to close the water budget at the global scale? Journal Article Hydrology and Earth System Sciences, 26 , pp. 35-54, 2022, ISSN: 1607-7938. Abstract | Links | BibTeX | Tags: @article{Lehmann2022,
title = {How well are we able to close the water budget at the global scale?},
author = {Fanny Lehmann and Bramha Dutt Vishwakarma and Jonathan Bamber},
url = {https://hess.copernicus.org/articles/26/35/2022/},
doi = {10.5194/hess-26-35-2022},
issn = {1607-7938},
year = {2022},
date = {2022-01-01},
journal = {Hydrology and Earth System Sciences},
volume = {26},
pages = {35-54},
abstract = { Abstract. The water budget equation describes the exchange of water between the land, ocean, and atmosphere. Being able to adequately close the water budget gives confidence in our ability to model and/or observe the spatio-temporal variations in the water cycle and its components. Due to advances in observation techniques, satellite sensors, and modelling, a number of data products are available that represent the components of water budget in both space and time. Despite these advances, closure of the water budget at the global scale has been elusive. In this study, we attempt to close the global water budget using precipitation, evapotranspiration, and runoff data at the catchment scale. The large number of recent state-of-the-art datasets provides a new evaluation of well-used datasets. These estimates are compared to terrestrial water storage (TWS) changes as measured by the Gravity Recovery And Climate Experiment (GRACE) satellite mission. We investigated 189 river basins covering more than 90 % of the continental land area. TWS changes derived from the water balance equation were compared against GRACE data using two metrics: the Nash–Sutcliffe efficiency (NSE) and the cyclostationary NSE. These metrics were used to assess the performance of more than 1600 combinations of the various datasets considered. We found a positive NSE and cyclostationary NSE in 99 % and 62 % of the basins examined respectively. This means that TWS changes reconstructed from the water balance equation were more accurate than the long-term (NSE) and monthly (cyclostationary NSE) mean of GRACE time series in the corresponding basins. By analysing different combinations of the datasets that make up the water balance, we identified data products that performed well in certain regions based on, for example, climatic zone. We identified that some of the good results were obtained due to the cancellation of errors in poor estimates of water budget components. Therefore, we used coefficients of variation to determine the relative quality of a data product, which helped us to identify bad combinations giving us good results. In general, water budget components from ERA5-Land and the Catchment Land Surface Model (CLSM) performed better than other products for most climatic zones. Conversely, the latest version of CLSM, v2.2, performed poorly for evapotranspiration in snow-dominated catchments compared, for example, with its predecessor and other datasets available. Thus, the nature of the catchment dynamics and balance between components affects the optimum combination of datasets. For regional studies, the combination of datasets that provides the most realistic TWS for a basin will depend on its climatic conditions and factors that cannot be determined a priori. We believe that the results of this study provide a road map for studying the water budget at catchment scale. },
keywords = {},
pubstate = {published},
tppubtype = {article}
}
<p>Abstract. The water budget equation describes the exchange of water between the land, ocean, and atmosphere. Being able to adequately close the water budget gives confidence in our ability to model and/or observe the spatio-temporal variations in the water cycle and its components. Due to advances in observation techniques, satellite sensors, and modelling, a number of data products are available that represent the components of water budget in both space and time. Despite these advances, closure of the water budget at the global scale has been elusive. In this study, we attempt to close the global water budget using precipitation, evapotranspiration, and runoff data at the catchment scale. The large number of recent state-of-the-art datasets provides a new evaluation of well-used datasets. These estimates are compared to terrestrial water storage (TWS) changes as measured by the Gravity Recovery And Climate Experiment (GRACE) satellite mission. We investigated 189 river basins covering more than 90 % of the continental land area. TWS changes derived from the water balance equation were compared against GRACE data using two metrics: the Nash–Sutcliffe efficiency (NSE) and the cyclostationary NSE. These metrics were used to assess the performance of more than 1600 combinations of the various datasets considered. We found a positive NSE and cyclostationary NSE in 99 % and 62 % of the basins examined respectively. This means that TWS changes reconstructed from the water balance equation were more accurate than the long-term (NSE) and monthly (cyclostationary NSE) mean of GRACE time series in the corresponding basins. By analysing different combinations of the datasets that make up the water balance, we identified data products that performed well in certain regions based on, for example, climatic zone. We identified that some of the good results were obtained due to the cancellation of errors in poor estimates of water budget components. Therefore, we used coefficients of variation to determine the relative quality of a data product, which helped us to identify bad combinations giving us good results. In general, water budget components from ERA5-Land and the Catchment Land Surface Model (CLSM) performed better than other products for most climatic zones. Conversely, the latest version of CLSM, v2.2, performed poorly for evapotranspiration in snow-dominated catchments compared, for example, with its predecessor and other datasets available. Thus, the nature of the catchment dynamics and balance between components affects the optimum combination of datasets. For regional studies, the combination of datasets that provides the most realistic TWS for a basin will depend on its climatic conditions and factors that cannot be determined a priori. We believe that the results of this study provide a road map for studying the water budget at catchment scale.</p> |