BUILDING DAMAGE ASSESSMENT WITH DEEP LEARNING
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. Rece...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-05-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/1133/2022/isprs-archives-XLIII-B3-2022-1133-2022.pdf |
| _version_ | 1828347949230325760 |
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| author | S. May A. Dupuis A. Lagrange F. De Vieilleville C. Fernandez-Martin |
| author_facet | S. May A. Dupuis A. Lagrange F. De Vieilleville C. Fernandez-Martin |
| author_sort | S. May |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T00:47:37Z |
| format | Article |
| id | doaj.art-1133693fa3cc43a2b7521b338bfd4b17 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-14T00:47:37Z |
| publishDate | 2022-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-1133693fa3cc43a2b7521b338bfd4b172022-12-22T02:21:55ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342022-05-01XLIII-B3-20221133113810.5194/isprs-archives-XLIII-B3-2022-1133-2022BUILDING DAMAGE ASSESSMENT WITH DEEP LEARNINGS. May0A. Dupuis1A. Lagrange2F. De Vieilleville3C. Fernandez-Martin4CNES, Toulouse, FranceCNES, Toulouse, FranceAgenium Space, Toulouse, FranceAgenium Space, Toulouse, FranceAgenium Space, Toulouse, FranceGlobal 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.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/1133/2022/isprs-archives-XLIII-B3-2022-1133-2022.pdf |
| spellingShingle | S. May A. Dupuis A. Lagrange F. De Vieilleville C. Fernandez-Martin BUILDING DAMAGE ASSESSMENT WITH DEEP LEARNING The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | BUILDING DAMAGE ASSESSMENT WITH DEEP LEARNING |
| title_full | BUILDING DAMAGE ASSESSMENT WITH DEEP LEARNING |
| title_fullStr | BUILDING DAMAGE ASSESSMENT WITH DEEP LEARNING |
| title_full_unstemmed | BUILDING DAMAGE ASSESSMENT WITH DEEP LEARNING |
| title_short | BUILDING DAMAGE ASSESSMENT WITH DEEP LEARNING |
| title_sort | building damage assessment with deep learning |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B3-2022/1133/2022/isprs-archives-XLIII-B3-2022-1133-2022.pdf |
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