Publications

191 entries « 1 of 4 »

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.).

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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

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

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

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).

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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).).

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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).).

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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.).

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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

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.)).

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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).

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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)).

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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).).

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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

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

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).).

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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

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

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

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

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/).

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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.).

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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

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

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

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:

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:

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).).

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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)

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:

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)

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.).

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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).).

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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.

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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: .

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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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).

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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.

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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.

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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.

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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.

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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.

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Our project is done in close collaboration with the Technical University of Munich. In particular with the TUM Data Science in Earth Observation (Sipeo) group. The complete list of associated publications might be also interesting for you and is available here.

Artificial Intelligence for Earth Observation