Articles | Volume 17, issue 11
https://doi.org/10.5194/essd-17-6217-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-6217-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
18 Nov 2025
Data description paper |
| 18 Nov 2025
| 18 Nov 2025
Bright: a globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response
Hongruixuan Chen
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo, Japan
Jian Song
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo, Japan
Olivier Dietrich
Department of Photogrammetry and Remote Sensing, ETH Zürich, Zürich, Switzerland
Clifford Broni-Bediako
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo, Japan
Weihao Xuan
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo, Japan
Junjue Wang
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
Xinlei Shao
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
Yimin Wei
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
Junshi Xia
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo, Japan
Cuiling Lan
Microsoft Research Asia, Beijing, China
Konrad Schindler
Department of Photogrammetry and Remote Sensing, ETH Zürich, Zürich, Switzerland
Naoto Yokoya
CORRESPONDING AUTHOR
Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
RIKEN Center for Advanced Intelligence Project (AIP), RIKEN, Tokyo, Japan
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Samantha Biegel, Konrad Schindler, and Benjamin D. Stocker
EGUsphere, https://doi.org/10.5194/egusphere-2025-1617, https://doi.org/10.5194/egusphere-2025-1617, 2025
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Our work addresses the predictability of carbon absorption by ecosystems across the globe, particularly in dry regions. We compare 3 different models, including a deep learning model that can learn from past environmental conditions, and show that this helps improve predictions. Still, challenges remain in dry areas due to varying vulnerabilities to drought. As drought conditions intensify globally, it's crucial to understand the varying impacts on ecosystem function.
Elisabeth D. Hafner, Theodora Kontogianni, Rodrigo Caye Daudt, Lucien Oberson, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
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For many safety-related applications such as road management, well-documented avalanches are important. To enlarge the information, webcams may be used. We propose supporting the mapping of avalanches from webcams with a machine learning model that interactively works together with the human. Relying on that model, there is a 90% saving of time compared to the "traditional" mapping. This gives a better base for safety-critical decisions and planning in avalanche-prone mountain regions.
B. Xiang, T. Peters, T. Kontogianni, F. Vetterli, S. Puliti, R. Astrup, and K. Schindler
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1-W1-2023, 605–612, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-605-2023, https://doi.org/10.5194/isprs-annals-X-1-W1-2023-605-2023, 2023
Elisabeth D. Hafner, Frank Techel, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
Nat. Hazards Earth Syst. Sci., 23, 2895–2914, https://doi.org/10.5194/nhess-23-2895-2023, https://doi.org/10.5194/nhess-23-2895-2023, 2023
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Oftentimes when objective measurements are not possible, human estimates are used instead. In our study, we investigate the reproducibility of human judgement for size estimates, the mappings of avalanches from oblique photographs and remotely sensed imagery. The variability that we found in those estimates is worth considering as it may influence results and should be kept in mind for several applications.
O. Kantarcioglu, K. Schindler, and S. Kocaman
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 161–167, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-161-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-161-2023, 2023
Elisabeth D. Hafner, Patrick Barton, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 16, 3517–3530, https://doi.org/10.5194/tc-16-3517-2022, https://doi.org/10.5194/tc-16-3517-2022, 2022
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Knowing where avalanches occur is very important information for several disciplines, for example avalanche warning, hazard zonation and risk management. Satellite imagery can provide such data systematically over large regions. In our work we propose a machine learning model to automate the time-consuming manual mapping. Additionally, we investigate expert agreement for manual avalanche mapping, showing that our network is equally as good as the experts in identifying avalanches.
C. Stucker, B. Ke, Y. Yue, S. Huang, I. Armeni, and K. Schindler
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 193–201, https://doi.org/10.5194/isprs-annals-V-2-2022-193-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-193-2022, 2022
Y. Xie, K. Schindler, J. Tian, and X. X. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 247–254, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-247-2021, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-247-2021, 2021
Nico Lang, Andrea Irniger, Agnieszka Rozniak, Roni Hunziker, Jan Dirk Wegner, and Konrad Schindler
Hydrol. Earth Syst. Sci., 25, 2567–2597, https://doi.org/10.5194/hess-25-2567-2021, https://doi.org/10.5194/hess-25-2567-2021, 2021
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Grain size analysis is the key to understanding the sediment dynamics of river systems and is an important indicator for mitigating flood risk and preserving biodiversity in aquatic habitats. We propose GRAINet, a data-driven approach based on deep learning, to regress grain size distributions from georeferenced UAV images. This allows for a holistic analysis of entire gravel bars, resulting in robust grading curves and high-resolution maps of spatial grain size distribution at large scale.
Lucie A. Eberhard, Pascal Sirguey, Aubrey Miller, Mauro Marty, Konrad Schindler, Andreas Stoffel, and Yves Bühler
The Cryosphere, 15, 69–94, https://doi.org/10.5194/tc-15-69-2021, https://doi.org/10.5194/tc-15-69-2021, 2021
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In spring 2018 in the alpine Dischma valley (Switzerland), we tested different industrial photogrammetric platforms for snow depth mapping. These platforms were high-resolution satellites, an airplane, unmanned aerial systems and a terrestrial system. Therefore, this study gives a general overview of the accuracy and precision of the different photogrammetric platforms available in space and on earth and their use for snow depth mapping.
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Short summary
Natural disasters often damage buildings and threaten lives, especially in areas with limited resources. To help improve emergency response, we created a global dataset called BRIGHT using both optical and radar images to detect building damage in any weather. We tested many artificial intelligence models and showed how well they work in real disaster scenes. This work can guide better tools for future disaster recovery and help save lives faster.
Natural disasters often damage buildings and threaten lives, especially in areas with limited...
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