The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1133-2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1133-2022
31 May 2022
 | 31 May 2022

BUILDING DAMAGE ASSESSMENT WITH DEEP LEARNING

S. May, A. Dupuis, A. Lagrange, F. De Vieilleville, and C. Fernandez-Martin

Keywords: deep learning, building detection, damage assessment, natural disaster, semantic segmentation, siamese network

Abstract. Global warming modifies the climate balance. Warming parameters are observed by many Earth Observation satellite systems, and the huge amount of data modifies the way to process them. This paper presents a few studies relative to damage detection on buildings, occurred during natural disasters. Recent advances in deep learning techniques are used for the building detection such as EfficientNet networks. Additional networks as Siamese models are used to evaluate the damage level with pre- and post-event images. Different techniques to merge detection masks are described and compared to a multiclass segmentation network. Results are presented and performances of the different solutions are compared.