Unsupervised learning framework for region-based damage assessment on xBD, a large satellite imagery
Author:
Publisher
Springer Science and Business Media LLC
Subject
Earth and Planetary Sciences (miscellaneous),Atmospheric Science,Water Science and Technology
Link
https://link.springer.com/content/pdf/10.1007/s11069-023-06074-y.pdf
Reference53 articles.
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2. Bai Y, Hu J, Su J et al (2020) Pyramid pooling module-based semi-Siamese network: a benchmark model for assessing building damage from XBD satellite imagery datasets. Remote Sens 12(24):4055
3. Barber CB, Dobkin DP, Huhdanpaa H (1996) The quickhull algorithm for convex hulls. ACM Trans Math Softw 22(4):469–483. https://doi.org/10.1145/235815.235821
4. Boin JB, Roth N, Doshi J et al (2020) Multi-class segmentation under severe class imbalance: a case study in roof damage assessment. https://doi.org/10.48550/ARXIV.2010.07151
5. Bouchard I, Rancourt Mv, Aloise D, et al (2022) On transfer learning for building damage assessment from satellite imagery in emergency contexts. Remote Sens. https://doi.org/10.3390/rs14112532
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