{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/multimathbf3net-segmenting-flooded-buildings","title":"Multi$^{\\mathbf{3}}$Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery","arxiv_id":"1812.01756","date":"2018-12-05","proceeding":null,"authors":["Tim G. J. Rudner","Marc Rußwurm","Jakub Fil","Ramona Pelich","Benjamin Bischke","Veronika Kopackova","Piotr Bilinski"],"abstract":"We propose a novel approach for rapid segmentation of flooded buildings by\nfusing multiresolution, multisensor, and multitemporal satellite imagery in a\nconvolutional neural network. Our model significantly expedites the generation\nof satellite imagery-based flood maps, crucial for first responders and local\nauthorities in the early stages of flood events. By incorporating multitemporal\nsatellite imagery, our model allows for rapid and accurate post-disaster damage\nassessment and can be used by governments to better coordinate medium- and\nlong-term financial assistance programs for affected areas. The network\nconsists of multiple streams of encoder-decoder architectures that extract\nspatiotemporal information from medium-resolution images and spatial\ninformation from high-resolution images before fusing the resulting\nrepresentations into a single medium-resolution segmentation map of flooded\nbuildings. We compare our model to state-of-the-art methods for building\nfootprint segmentation as well as to alternative fusion approaches for the\nsegmentation of flooded buildings and find that our model performs best on both\ntasks. We also demonstrate that our model produces highly accurate segmentation\nmaps of flooded buildings using only publicly available medium-resolution data\ninstead of significantly more detailed but sparsely available very\nhigh-resolution data. We release the first open-source dataset of fully\npreprocessed and labeled multiresolution, multispectral, and multitemporal\nsatellite images of disaster sites along with our source code.","url_abs":"http://arxiv.org/abs/1812.01756v1","url_pdf":"http://arxiv.org/pdf/1812.01756v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"multimathbf3net-segmenting-flooded-buildings","repo_url":"https://github.com/FrontierDevelopmentLab/multi3net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"flooded-building-segmentation","task_name":"Flooded Building Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1812.01756","atlas_url":"https://app.syntology.ai/?focus=1812.01756","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}