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.
Publications
Publications
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). @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. | |
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 = { }, <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> | |
Gawlikowski, Jakob; Saha, Sudipan; Kruspe, Anna; Zhu, Xiao Xiang An Advanced Dirichlet Prior Network for Out-of-Distribution Detection in Remote Sensing Journal Article IEEE Transactions on Geoscience and Remote Sensing, 2022. BibTeX | Tags: @article{Gawlikowski2022, title = {An Advanced Dirichlet Prior Network for Out-of-Distribution Detection in Remote Sensing}, author = {Jakob Gawlikowski and Sudipan Saha and Anna Kruspe and Xiao Xiang Zhu}, year = {2022}, date = {2022-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Saha, Sudipan; Zhao, Shan; Zhu, Xiao Xiang Multi-target domain adaptation for remote sensing classification using graph neural network Journal Article IEEE Geoscience and Remote Sensing Letters, 2022. BibTeX | Tags: @article{Saha2022, title = {Multi-target domain adaptation for remote sensing classification using graph neural network}, author = {Sudipan Saha and Shan Zhao and Xiao Xiang Zhu}, year = {2022}, date = {2022-01-01}, journal = {IEEE Geoscience and Remote Sensing Letters}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Roschlaub, Robert; Glock, Clemens; Möst, Karin; Hümmer, Frank; Li, Qingyu; Auer, Stefan; Kruspe, Anna; Zhu, Xiao Xiang Implementierung einer KI-Infrastruktur zur automatisierten Erkennung von landesweiten Gebäudeveränderungen aus Luftbildern Journal Article ZFV - Zeitschrift für Geodasie, Geoinformation und Landmanagement, 2022. Abstract | Links | BibTeX | Tags: @article{Roschlaub2022, title = {Implementierung einer KI-Infrastruktur zur automatisierten Erkennung von landesweiten Gebäudeveränderungen aus Luftbildern}, author = {Robert Roschlaub and Clemens Glock and Karin Möst and Frank Hümmer and Qingyu Li and Stefan Auer and Anna Kruspe and Xiao Xiang Zhu}, url = {https://elib.dlr.de/185326/}, year = {2022}, date = {2022-01-01}, journal = {ZFV - Zeitschrift für Geodasie, Geoinformation und Landmanagement}, publisher = {Wißner-Verlag}, abstract = {Aufbauend auf einer vorangegangenen Studie über den Einsatz von Künstlicher Intelligenz (KI) zur Detektion von Gebäudeveränderungen im amtlichen Liegenschaftskataster, wird in diesem Beitrag die Transformation von der Projektphase in einen Produktivbetrieb vorgestellt, es werden verschiedene KI-Architekturen angesprochen, miteinander verglichen und eine Auswahlentscheidung gegeben. Die erzielten Ergebnisse einer landesweiten Gebäudedetektion werden gemeinsam mit Untersuchungen zu den Trainingsläufen, zur Performance und den von einem Vermessungsamt validierten KI-Ergebnissen präsentiert.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Aufbauend auf einer vorangegangenen Studie über den Einsatz von Künstlicher Intelligenz (KI) zur Detektion von Gebäudeveränderungen im amtlichen Liegenschaftskataster, wird in diesem Beitrag die Transformation von der Projektphase in einen Produktivbetrieb vorgestellt, es werden verschiedene KI-Architekturen angesprochen, miteinander verglichen und eine Auswahlentscheidung gegeben. Die erzielten Ergebnisse einer landesweiten Gebäudedetektion werden gemeinsam mit Untersuchungen zu den Trainingsläufen, zur Performance und den von einem Vermessungsamt validierten KI-Ergebnissen präsentiert. | |
Stadtler, Scarlet; Betancourt, Clara; Roscher, Ribana Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset Journal Article Machine Learning and Knowledge Extraction, 4 , pp. 150-171, 2022, ISSN: 2504-4990. Abstract | Links | BibTeX | Tags: @article{Stadtler2022, title = {Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset}, author = {Scarlet Stadtler and Clara Betancourt and Ribana Roscher}, url = {https://www.mdpi.com/2504-4990/4/1/8}, doi = {10.3390/make4010008}, issn = {2504-4990}, year = {2022}, date = {2022-01-01}, journal = {Machine Learning and Knowledge Extraction}, volume = {4}, pages = {150-171}, publisher = {Multidisciplinary Digital Publishing Institute}, abstract = { Air quality is relevant to society because it poses environmental risks to humans and nature. We use explainable machine learning in air quality research by analyzing model predictions in relation to the underlying training data. The data originate from worldwide ozone observations, paired with geospatial data. We use two different architectures: a neural network and a random forest trained on various geospatial data to predict multi-year averages of the air pollutant ozone. To understand how both models function, we explain how they represent the training data and derive their predictions. By focusing on inaccurate predictions and explaining why these predictions fail, we can (i) identify underrepresented samples, (ii) flag unexpected inaccurate predictions, and (iii) point to training samples irrelevant for predictions on the test set. Based on the underrepresented samples, we suggest where to build new measurement stations. We also show which training samples do not substantially contribute to the model performance. This study demonstrates the application of explainable machine learning beyond simply explaining the trained model. },keywords = {}, pubstate = {published}, tppubtype = {article} } <p>Air quality is relevant to society because it poses environmental risks to humans and nature. We use explainable machine learning in air quality research by analyzing model predictions in relation to the underlying training data. The data originate from worldwide ozone observations, paired with geospatial data. We use two different architectures: a neural network and a random forest trained on various geospatial data to predict multi-year averages of the air pollutant ozone. To understand how both models function, we explain how they represent the training data and derive their predictions. By focusing on inaccurate predictions and explaining why these predictions fail, we can (i) identify underrepresented samples, (ii) flag unexpected inaccurate predictions, and (iii) point to training samples irrelevant for predictions on the test set. Based on the underrepresented samples, we suggest where to build new measurement stations. We also show which training samples do not substantially contribute to the model performance. This study demonstrates the application of explainable machine learning beyond simply explaining the trained model.</p> | |
Betancourt, C; Stomberg, T T; Edrich, A -K; Patnala, A; Schultz, M G; Roscher, R; Kowalski, J; Stadtler, S Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties Journal Article Geoscientific Model Development Discussions, 2022 , pp. 1-36, 2022. @article{Betancourt2022, title = {Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties}, author = {C Betancourt and T T Stomberg and A -K Edrich and A Patnala and M G Schultz and R Roscher and J Kowalski and S Stadtler}, url = {https://gmd.copernicus.org/preprints/gmd-2022-2/}, doi = {10.5194/gmd-2022-2}, year = {2022}, date = {2022-01-01}, journal = {Geoscientific Model Development Discussions}, volume = {2022}, pages = {1-36}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
2021 |
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 | Hua, Yuansheng; Mou, Lichao; Lin, Jianzhe; Heidler, Konrad; Zhu, Xiao Xiang Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks Journal Article ISPRS Journal of Photogrammetry and Remote Sensing, 2021. BibTeX | Tags: @article{hua2021prototype, title = {Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks}, author = {Yuansheng Hua and Lichao Mou and Jianzhe Lin and Konrad Heidler and Xiao Xiang Zhu}, year = {2021}, date = {2021-04-09}, journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
 | Klemmer, Konstantin; Saha, Sudipan; Kahl, Matthias; Xu, Tianlin; Zhu, Xiao Xiang Generative modeling of spatio-temporal weather patterns with extreme event conditioning Inproceedings AI: Modeling Oceans and Climate Change (AIMOCC 2021) Workshop, ICLR 2021, 2021. Abstract | Links | BibTeX | Tags: @inproceedings{Klemmer2021, title = {Generative modeling of spatio-temporal weather patterns with extreme event conditioning}, author = {Konstantin Klemmer and Sudipan Saha and Matthias Kahl and Tianlin Xu and Xiao Xiang Zhu}, url = {http://arxiv.org/abs/2104.12469}, year = {2021}, date = {2021-04-01}, booktitle = {AI: Modeling Oceans and Climate Change (AIMOCC 2021) Workshop, ICLR 2021}, abstract = {Deep generative models are increasingly used to gain insights in the geospatial data domain, e.g., for climate data. However, most existing approaches work with temporal snapshots or assume 1D time-series; few are able to capture spatio-temporal processes simultaneously. Beyond this, Earth-systems data often exhibit highly irregular and complex patterns, for example caused by extreme weather events. Because of climate change, these phenomena are only increasing in frequency. Here, we proposed a novel GAN-based approach for generating spatio-temporal weather patterns conditioned on detected extreme events. Our approach augments GAN generator and discriminator with an encoded extreme weather event segmentation mask. These segmentation masks can be created from raw input using existing event detection frameworks. As such, our approach is highly modular and can be combined with custom GAN architectures. We highlight the applicability of our proposed approach in experiments with real-world surface radiation and zonal wind data.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Deep generative models are increasingly used to gain insights in the geospatial data domain, e.g., for climate data. However, most existing approaches work with temporal snapshots or assume 1D time-series; few are able to capture spatio-temporal processes simultaneously. Beyond this, Earth-systems data often exhibit highly irregular and complex patterns, for example caused by extreme weather events. Because of climate change, these phenomena are only increasing in frequency. Here, we proposed a novel GAN-based approach for generating spatio-temporal weather patterns conditioned on detected extreme events. Our approach augments GAN generator and discriminator with an encoded extreme weather event segmentation mask. These segmentation masks can be created from raw input using existing event detection frameworks. As such, our approach is highly modular and can be combined with custom GAN architectures. We highlight the applicability of our proposed approach in experiments with real-world surface radiation and zonal wind data. |
 | Perko, Roland; Klopschitz, Manfred; Almer, Alexander; Roth, Peter M Critical Aspects of Person Couting and Density Estimation Journal Article Journal of Imaging, 7 (2), pp. 21, 2021. BibTeX | Tags: @article{perko21a, title = {Critical Aspects of Person Couting and Density Estimation}, author = {Roland Perko and Manfred Klopschitz and Alexander Almer and Peter M Roth}, year = {2021}, date = {2021-01-01}, journal = {Journal of Imaging}, volume = {7}, number = {2}, pages = {21}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
 | Basirat, Mina; Roth, Peter M S*ReLU: Learning Piecewise Linear Activation Functions via ParticleSwarm Optimization Inproceedings International Conf. on Computer Vision Theory and Applications, 2021. BibTeX | Tags: @inproceedings{basirat21ab, title = {S*ReLU: Learning Piecewise Linear Activation Functions via ParticleSwarm Optimization}, author = {Mina Basirat and Peter M Roth}, year = {2021}, date = {2021-01-01}, booktitle = {International Conf. on Computer Vision Theory and Applications}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
 | Editors:Camps-Valls, Gustau; Tuia, Devis; Zhu, Xiao Xiang; Reichstein, Markus Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences Book Wiley & Sons, 2021, ISBN: 978-1-119-64614-3. BibTeX | Tags: @book{CampsValls21wiley, title = {Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences}, author = {Gustau Editors:Camps-Valls and Devis Tuia and Xiao Xiang Zhu and Markus Reichstein }, isbn = {978-1-119-64614-3}, year = {2021}, date = {2021-01-01}, publisher = {Wiley & Sons}, keywords = {}, pubstate = {published}, tppubtype = {book} } |
 | Mou, Lichao; Saha, Sudipan; Hua, Yuansheng; Bovolo, Francesca; Bruzzone, Lorenzo; Zhu, Xiao Xiang Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification Journal Article IEEE Transactions on Geoscience and Remote Sensing, 2021. BibTeX | Tags: @article{deepReinforcementTgrs2021, title = {Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification}, author = {Lichao Mou and Sudipan Saha and Yuansheng Hua and Francesca Bovolo and Lorenzo Bruzzone and Xiao Xiang Zhu}, year = {2021}, date = {2021-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
 | Saha, Sudipan; Banerjee, Biplab; Zhu, Xiao Xiang Trusting Small Training Dataset for Supervised Change Detection Conference IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted), IEEE, 2021. BibTeX | Tags: @conference{trustingSmallDatasetSudipanIgarss2021, title = {Trusting Small Training Dataset for Supervised Change Detection}, author = {Sudipan Saha and Biplab Banerjee and Xiao Xiang Zhu}, year = {2021}, date = {2021-01-01}, booktitle = {IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted)}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
 | Prexl, Jonathan; Saha, Sudipan; Zhu, Xiao Xiang Mitigating Spatial and Spectral Differences for Change Detection using Super-resolution and Unsupervised learning Conference IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted), IEEE, 2021. BibTeX | Tags: @conference{mitigatingJonathanIgarss2021, title = {Mitigating Spatial and Spectral Differences for Change Detection using Super-resolution and Unsupervised learning}, author = {Jonathan Prexl and Sudipan Saha and Xiao Xiang Zhu}, year = {2021}, date = {2021-01-01}, booktitle = {IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted)}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
 | Gawlikowski, Jakob; Saha, Sudipan; Kruspe, Anna; Zhu, Xiao Xiang Towards Out-of-distribution Detection for Remote Sensing Conference IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted), IEEE, 2021. BibTeX | Tags: @conference{towardsGawlikowskiIgarss2021, title = {Towards Out-of-distribution Detection for Remote Sensing}, author = {Jakob Gawlikowski and Sudipan Saha and Anna Kruspe and Xiao Xiang Zhu}, year = {2021}, date = {2021-01-01}, booktitle = {IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted)}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
 | Saha, Sudipan; Kondmann, Lukas; Zhu, Xiao Xiang Deep No Learning Approach for Unsupervised Change Detection in Hyperspectral Images Conference XXIV ISPRS Congress 2021, 2021. BibTeX | Tags: @conference{deepNoLearningSudipanIsprs2021, title = {Deep No Learning Approach for Unsupervised Change Detection in Hyperspectral Images}, author = {Sudipan Saha and Lukas Kondmann and Xiao Xiang Zhu}, year = {2021}, date = {2021-01-01}, booktitle = {XXIV ISPRS Congress 2021}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
 | Ebel, Patrick; Saha, Sudipan; Zhu, Xiao Xiang Fusing Multi-modal Data for Supervised Change Detection Conference XXIV ISPRS Congress 2021, 2021. BibTeX | Tags: @conference{fusingPatrickIsprs2021, title = {Fusing Multi-modal Data for Supervised Change Detection}, author = {Patrick Ebel and Sudipan Saha and Xiao Xiang Zhu}, year = {2021}, date = {2021-01-01}, booktitle = {XXIV ISPRS Congress 2021}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
 | Nandy, Jay; Saha, Sudipan; Hsu, Wynne; Lee, Mong Li; Zhu, Xiao Xiang Covariate Shift Adaptation for Adversarially Robust Classifier Conference ICLR 2021 Workshop on Security and Safety in Machine Learning Systems, 2021. BibTeX | Tags: @conference{covariateJayIclrW2021, title = {Covariate Shift Adaptation for Adversarially Robust Classifier}, author = {Jay Nandy and Sudipan Saha and Wynne Hsu and Mong Li Lee and Xiao Xiang Zhu}, year = {2021}, date = {2021-01-01}, booktitle = {ICLR 2021 Workshop on Security and Safety in Machine Learning Systems}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
| Gawlikowski, Jakob; Saha, Sudipan; Kruspe, Anna; Zhu, Xiao Xiang Out-of-distribution Detection in Satellite Image Classification Conference RobustML workshop at ICLR 2021, 2021. BibTeX | Tags: @conference{oodJakobIclrW2021, title = {Out-of-distribution Detection in Satellite Image Classification}, author = {Jakob Gawlikowski and Sudipan Saha and Anna Kruspe and Xiao Xiang Zhu}, year = {2021}, date = {2021-01-01}, booktitle = {RobustML workshop at ICLR 2021}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
 | Kochupillai, Mrinalini; Gallersdörfer, Ulrich; Köninger, Julia; Beck, Roman Incentivizing research & innovation with agrobiodiversity conserved in situ: Possibilities and limitations of a blockchain-based solution Journal Article Journal of Cleaner Production, 309 , pp. 127155, 2021, ISSN: 0959-6526. BibTeX | Tags: @article{RN2468, title = {Incentivizing research & innovation with agrobiodiversity conserved in situ: Possibilities and limitations of a blockchain-based solution}, author = {Mrinalini Kochupillai and Ulrich Gallersdörfer and Julia Köninger and Roman Beck}, issn = {0959-6526}, year = {2021}, date = {2021-01-01}, journal = {Journal of Cleaner Production}, volume = {309}, pages = {127155}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
 | Kochupillai, M Creating a Digital Marketplace for Agrobiodiversity and Plant Genetic Sequence Data: Legal and Ethical Considerations of an AI and Blackchain Based Solution, Conference paper, Towards Responsible Plant Data Linkage: Global Challenges for Food Security and Governance, Alan Turing Institute and University of Exeter, March 2021 (forthcoming in an edited volume with Springer Nature) Conference 2021. BibTeX | Tags: @conference{RN2474b, title = {Creating a Digital Marketplace for Agrobiodiversity and Plant Genetic Sequence Data: Legal and Ethical Considerations of an AI and Blackchain Based Solution, Conference paper, Towards Responsible Plant Data Linkage: Global Challenges for Food Security and Governance, Alan Turing Institute and University of Exeter, March 2021 (forthcoming in an edited volume with Springer Nature)}, author = {M Kochupillai}, year = {2021}, date = {2021-01-01}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
 | Saha, Sudipan; Ebel, Patrick; Zhu, Xiao Xiang Self-supervised multisensor change detection Journal Article IEEE Transactions on Geoscience and Remote Sensing, 2021. BibTeX | Tags: @article{selfSupervisedMultisensorTgrs2021, title = {Self-supervised multisensor change detection}, author = {Sudipan Saha and Patrick Ebel and Xiao Xiang Zhu}, year = {2021}, date = {2021-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
 | Saha, Sudipan; Ahmad, Tahir Federated transfer learning: Concept and applications Journal Article Intelligenza Artificiale, 15 (1), pp. 35–44, 2021. BibTeX | Tags: @article{ftlSurvey2021, title = {Federated transfer learning: Concept and applications}, author = {Sudipan Saha and Tahir Ahmad}, year = {2021}, date = {2021-01-01}, journal = {Intelligenza Artificiale}, volume = {15}, number = {1}, pages = {35--44}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Saha, Sudipan; Zhu, Xiao Xiang Patch-level unsupervised planetary change detection Journal Article IEEE Geoscience and Remote Sensing Letters, 2021. BibTeX | Tags: @article{Saha2021, title = {Patch-level unsupervised planetary change detection}, author = {Sudipan Saha and Xiao Xiang Zhu}, year = {2021}, date = {2021-01-01}, journal = {IEEE Geoscience and Remote Sensing Letters}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Saha, Sudipan; Mou, Lichao; Shahzad, Muhammad; Zhu, Xiao Xiang Segmentation of VHR EO Images using Unsupervised Learning Inproceedings 2021. BibTeX | Tags: @inproceedings{Saha2021b, title = {Segmentation of VHR EO Images using Unsupervised Learning}, author = {Sudipan Saha and Lichao Mou and Muhammad Shahzad and Xiao Xiang Zhu}, year = {2021}, date = {2021-01-01}, journal = {ECML PKDD 2021 workshop Machine Learning for Earth Observation (MACLEAN)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } | |
Ahmed, Nouman; Saha, Sudipan; Mohsin, Maaz; Shahzad, Muhammad; Zhu, Xiao Xiang Progressive Unsupervised Deep Transfer Learning for Forest Mapping in Satellite Image Inproceedings 2021. BibTeX | Tags: @inproceedings{Ahmed2021, title = {Progressive Unsupervised Deep Transfer Learning for Forest Mapping in Satellite Image}, author = {Nouman Ahmed and Sudipan Saha and Maaz Mohsin and Muhammad Shahzad and Xiao Xiang Zhu}, year = {2021}, date = {2021-01-01}, journal = {ICCV 2021 workshop on Learning to Understand Aerial Images (LUAI)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } | |
Saha, Sudipan; Kondmann, Lukas; Song, Qian; Zhu, Xiao Xiang Change Detection in Hyperdimensional Images using Untrained Models Journal Article IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021. BibTeX | Tags: @article{Saha2021c, title = {Change Detection in Hyperdimensional Images using Untrained Models}, author = {Sudipan Saha and Lukas Kondmann and Qian Song and Xiao Xiang Zhu}, year = {2021}, date = {2021-01-01}, journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, publisher = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Kondmann, Lukas; Toker, Aysim; Saha, Sudipan; Schölkopf, Bernhard; Leal-Taixé, Laura; Zhu, Xiao Xiang Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images Journal Article arXiv preprint arXiv:2110.02068, 2021. BibTeX | Tags: @article{Kondmann2021, title = {Spatial Context Awareness for Unsupervised Change Detection in Optical Satellite Images}, author = {Lukas Kondmann and Aysim Toker and Sudipan Saha and Bernhard Schölkopf and Laura Leal-Taixé and Xiao Xiang Zhu}, year = {2021}, date = {2021-01-01}, journal = {arXiv preprint arXiv:2110.02068}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Yuan, Zhenghang; Mou, Lichao; Zhu, Xiao Xiang Self-Paced Curriculum Learning for Visual Question Answering on Remote Sensing Data Conference IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) , Institute of Electrical and Electronics Engineers (IEEE), 2021, ISBN: 9781665403696. Abstract | Links | BibTeX | Tags: @conference{Yuan2021, title = {Self-Paced Curriculum Learning for Visual Question Answering on Remote Sensing Data}, author = {Zhenghang Yuan and Lichao Mou and Xiao Xiang Zhu}, doi = {10.1109/IGARSS47720.2021.9553624}, isbn = {9781665403696}, year = {2021}, date = {2021-01-01}, booktitle = {IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) }, journal = {IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) }, pages = {2999-3002}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, abstract = {Answering questions with natural language by extracting information from image has great potential in various applications. Although visual question answering (VQA) for natural image has been broadly studied, VQA for remote sensing data is still in the early research stage. For the same remote sensing image, there exist questions with dramatically different difficulty-levels. Treating these questions equally may mislead the model and limit the VQA model performance. Considering this problem, in this work, we propose a self-paced curriculum learning (SPCL) based VQA model with hard and soft weighting strategies for remote sensing data. Like human learning process, the model is trained from easy to hard question samples gradually. Extensive experimental results on two datasets demonstrate that the proposed training method can achieve promising performance.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Answering questions with natural language by extracting information from image has great potential in various applications. Although visual question answering (VQA) for natural image has been broadly studied, VQA for remote sensing data is still in the early research stage. For the same remote sensing image, there exist questions with dramatically different difficulty-levels. Treating these questions equally may mislead the model and limit the VQA model performance. Considering this problem, in this work, we propose a self-paced curriculum learning (SPCL) based VQA model with hard and soft weighting strategies for remote sensing data. Like human learning process, the model is trained from easy to hard question samples gradually. Extensive experimental results on two datasets demonstrate that the proposed training method can achieve promising performance. | |
Köninger, Julia; Lugato, Emanuele; Panagos, Panos; Kochupillai, Mrinalini; Orgiazzi, Alberto; Briones, Maria J I Manure management and soil biodiversity: Towards more sustainable food systems in the EU Journal Article Agricultural Systems, 194 , 2021, ISSN: 0308521X. Abstract | Links | BibTeX | Tags: @article{, title = {Manure management and soil biodiversity: Towards more sustainable food systems in the EU}, author = {Julia Köninger and Emanuele Lugato and Panos Panagos and Mrinalini Kochupillai and Alberto Orgiazzi and Maria J I Briones}, doi = {10.1016/J.AGSY.2021.103251}, issn = {0308521X}, year = {2021}, date = {2021-01-01}, journal = {Agricultural Systems}, volume = {194}, publisher = {Elsevier Ltd}, abstract = {CONTEXT: In the European Union (EU-27) and UK, animal farming generated annually more than 1.4 billion tonnes of manure during the period 2016–2019. Of this, more than 90% is directly re-applied to soils as organic fertiliser. Manure promotes plant growth, provides nutritious food to soil organisms, adds genetic and functional diversity to soils and improves the chemical and physical soil properties. However, it can also cause pollution by introducing toxic elements (i.e., heavy metals, antibiotics, pathogens) and contribute to nutrient losses. Soil organisms play an essential role in manure transformation into the soil and the degradation of any potential toxic constitutes; however, manure management practices often neglect soil biodiversity. OBJECTIVE: In this review, we explored the impact of manure from farmed animals on soil biodiversity by considering factors that determine the effects of manure and vice versa. By evaluating manure's potential to enhance soil biodiversity, but also its environmental risks, we assessed current and future EU policy and legislations with the ultimate aim of providing recommendations that can enable a more sustainable management of farm manures. METHODS: This review explored the relationship between manure and soil biodiversity by considering 407 published papers and relevant legislative provisions. In addition, we evaluated whether benefits and risks on soil biodiversity are considered in manure management. Thereafter, we analysed the current legislation in the European Union relevant to manure, an important driver for its treatment, application and storage. RESULTS AND CONCLUSIONS: This review found that coupling manure management with soil biodiversity can mitigate present and future environmental risks. Our analyses showed that manure quality is more important to soil biodiversity than manure quantity and therefore, agricultural practices that protect and promote soil biodiversity with the application of appropriate, high-quality manure or biostimulant preparations based on manure, could accelerate the move towards more sustainable food production systems. Soil biodiversity needs to be appropriately factored in when assessing manure amendments to provide better guidelines on the use of manure and to reduce costs and environmental risks. However, radical changes in current philosophies and practices are needed so that soil biodiversity can be enhanced by manure management. SIGNIFICANCE: Manure quality in the EU requires greater attention, calling for more targeted policies. Our proposed approach could be applied by European Union Member States to include soil protection measures in national legislation, and at the EU level, can enable the implementation of strategic goals.}, keywords = {}, pubstate = {published}, tppubtype = {article} } CONTEXT: In the European Union (EU-27) and UK, animal farming generated annually more than 1.4 billion tonnes of manure during the period 2016–2019. Of this, more than 90% is directly re-applied to soils as organic fertiliser. Manure promotes plant growth, provides nutritious food to soil organisms, adds genetic and functional diversity to soils and improves the chemical and physical soil properties. However, it can also cause pollution by introducing toxic elements (i.e., heavy metals, antibiotics, pathogens) and contribute to nutrient losses. Soil organisms play an essential role in manure transformation into the soil and the degradation of any potential toxic constitutes; however, manure management practices often neglect soil biodiversity. OBJECTIVE: In this review, we explored the impact of manure from farmed animals on soil biodiversity by considering factors that determine the effects of manure and vice versa. By evaluating manure's potential to enhance soil biodiversity, but also its environmental risks, we assessed current and future EU policy and legislations with the ultimate aim of providing recommendations that can enable a more sustainable management of farm manures. METHODS: This review explored the relationship between manure and soil biodiversity by considering 407 published papers and relevant legislative provisions. In addition, we evaluated whether benefits and risks on soil biodiversity are considered in manure management. Thereafter, we analysed the current legislation in the European Union relevant to manure, an important driver for its treatment, application and storage. RESULTS AND CONCLUSIONS: This review found that coupling manure management with soil biodiversity can mitigate present and future environmental risks. Our analyses showed that manure quality is more important to soil biodiversity than manure quantity and therefore, agricultural practices that protect and promote soil biodiversity with the application of appropriate, high-quality manure or biostimulant preparations based on manure, could accelerate the move towards more sustainable food production systems. Soil biodiversity needs to be appropriately factored in when assessing manure amendments to provide better guidelines on the use of manure and to reduce costs and environmental risks. However, radical changes in current philosophies and practices are needed so that soil biodiversity can be enhanced by manure management. SIGNIFICANCE: Manure quality in the EU requires greater attention, calling for more targeted policies. Our proposed approach could be applied by European Union Member States to include soil protection measures in national legislation, and at the EU level, can enable the implementation of strategic goals. | |
 | Kochupillai, M Outline of a Novel Approach for Indentifying Ethical Issues in Early Stages of AI4EO Research,Conference paper for the IEEE’s International Geoscience and Remote Sensing Symposium (IGARSS) Conference 2021. BibTeX | Tags: @conference{RN2472, title = {Outline of a Novel Approach for Indentifying Ethical Issues in Early Stages of AI4EO Research,Conference paper for the IEEE’s International Geoscience and Remote Sensing Symposium (IGARSS)}, author = {M Kochupillai}, year = {2021}, date = {2021-00-00}, journal = {Conference paper for the IEEE’s International Geoscience and Remote Sensing Symposium (IGARSS),}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
 | Hua, Y; Mou, L; Jin, P; Zhu, X X MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images Journal Article IEEE Transactions on Geoscience and Remote Sensing, 2021. BibTeX | Tags: @article{hua2021multiscene, title = {MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images}, author = {Y Hua and L Mou and P Jin and X X Zhu}, year = {2021}, date = {2021-00-00}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2020 |
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 | Mou, Lichao; Hua, Yuansheng; Jin, Pu; Zhu, Xiao Xiang ERA: A dataset and deep learning benchmark for event recognition in aerial videos Journal Article IEEE Geoscience and Remote Sensing Magazine, 2020, (in press). BibTeX | Tags: @article{Mou2020, title = {ERA: A dataset and deep learning benchmark for event recognition in aerial videos}, author = {Lichao Mou and Yuansheng Hua and Pu Jin and Xiao Xiang Zhu}, year = {2020}, date = {2020-01-01}, journal = {IEEE Geoscience and Remote Sensing Magazine}, note = {in press}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
 | Mou, Lichao; Hua, Yuansheng; Zhu, Xiao Xiang Relation matters: Relational context-aware fully convolutional network for semantic segmentation of high resolution aerial images Journal Article IEEE Transactions on Geoscience and Remote Sensing, 2020, (in press). BibTeX | Tags: @article{Mou2020a, title = {Relation matters: Relational context-aware fully convolutional network for semantic segmentation of high resolution aerial images}, author = {Lichao Mou and Yuansheng Hua and Xiao Xiang Zhu}, year = {2020}, date = {2020-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, note = {in press}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
 | Hua, Yuansheng; Mou, Lichao; Zhu, Xiao Xiang Relation network for multilabel aerial image classification Journal Article IEEE Transactions on Geoscience and Remote Sensing, 58 (7), pp. 4558-4572, 2020. BibTeX | Tags: @article{Hua2020, title = {Relation network for multilabel aerial image classification}, author = {Yuansheng Hua and Lichao Mou and Xiao Xiang Zhu}, year = {2020}, date = {2020-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {58}, number = {7}, pages = {4558-4572}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
 | Rußwurm, Marc; Ali, Mohsin; Zhu, Xiaoxiang; Gal, Yarin; Körner, Marco Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models Inproceedings IGARSS 2020 IEEE International Geoscience and Remote Sensing Symposium, IEEE 2020. BibTeX | Tags: @inproceedings{russwurm2020c, title = {Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models}, author = {Marc Rußwurm and Mohsin Ali and Xiaoxiang Zhu and Yarin Gal and Marco Körner}, year = {2020}, date = {2020-01-01}, booktitle = {IGARSS 2020 IEEE International Geoscience and Remote Sensing Symposium}, organization = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
 | Kochupillai, Mrinalini; Lütge, Christoph; Poszler, Franziska Programming Away Human Rights and Responsibilities?“The Moral Machine Experiment” and the Need for a More “Humane” AV Future Journal Article NanoEthics, 14 (3), pp. 285-299, 2020, ISSN: 1871-4765. BibTeX | Tags: @article{RN2469, title = {Programming Away Human Rights and Responsibilities?“The Moral Machine Experiment” and the Need for a More “Humane” AV Future}, author = {Mrinalini Kochupillai and Christoph Lütge and Franziska Poszler}, issn = {1871-4765}, year = {2020}, date = {2020-01-01}, journal = {NanoEthics}, volume = {14}, number = {3}, pages = {285-299}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
 | Brinkmann, Johannes; Kochupillai, Mrinalini Law, Business, and Legitimacy Book Chapter pp. 489-507, 2020, ISSN: 3030146219. BibTeX | Tags: @inbook{RN2470, title = {Law, Business, and Legitimacy}, author = {Johannes Brinkmann and Mrinalini Kochupillai}, issn = {3030146219}, year = {2020}, date = {2020-01-01}, journal = {Handbook of Business Legitimacy: Responsibility, Ethics and Society}, pages = {489-507}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } |