{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T04:34:34Z","timestamp":1780461274004,"version":"3.54.1"},"reference-count":79,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,31]],"date-time":"2023-12-31T00:00:00Z","timestamp":1703980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>When natural disasters occur, timely and accurate building damage assessment maps are vital for disaster management responders to organize their resources efficiently. Pairs of pre- and post-disaster remote sensing imagery have been recognized as invaluable data sources that provide useful information for building damage identification. Recently, deep learning-based semantic segmentation models have been widely and successfully applied to remote sensing imagery for building damage assessment tasks. In this study, a two-stage, dual-branch, UNet architecture, with shared weights between two branches, is proposed to address the inaccuracies in building footprint localization and per-building damage level classification. A newly introduced selective kernel module improves the performance of the model by enhancing the extracted features and applying adaptive receptive field variations. The xBD dataset is used to train, validate, and test the proposed model based on widely used evaluation metrics such as F1-score and Intersection over Union (IoU). Overall, the experiments and comparisons demonstrate the superior performance of the proposed model. In addition, the results are further confirmed by evaluating the geographical transferability of the proposed model on a completely unseen dataset from a new region (Bam city earthquake in 2003).<\/jats:p>","DOI":"10.3390\/rs16010182","type":"journal-article","created":{"date-parts":[[2023,12,31]],"date-time":"2023-12-31T10:01:06Z","timestamp":1704016866000},"page":"182","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["BD-SKUNet: Selective-Kernel UNets for Building Damage Assessment in High-Resolution Satellite Images"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3920-2390","authenticated-orcid":false,"given":"Seyed Ali","family":"Ahmadi","sequence":"first","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19697, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3329-5063","authenticated-orcid":false,"given":"Ali","family":"Mohammadzadeh","sequence":"additional","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19697, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7321-4590","authenticated-orcid":false,"given":"Naoto","family":"Yokoya","sequence":"additional","affiliation":[{"name":"Department of Complexity Science and Engineering, The University of Tokyo, Chiba 277-8561, Japan"},{"name":"Geoinformatics Team, RIKEN Center for Advanced Intelligence Project (AIP), Tokyo 103-0027, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8406-683X","authenticated-orcid":false,"given":"Arsalan","family":"Ghorbanian","sequence":"additional","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19697, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,31]]},"reference":[{"key":"ref_1","unstructured":"Ritchie, H., and Roser, M. (2023, April 27). Natural Disasters. Available online: https:\/\/ourworldindata.org\/natural-disasters."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ji, M., Liu, L., Zhang, R., and Buchroithner, M.F. (2020). Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model. Appl. Sci., 10.","DOI":"10.3390\/app10020602"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102282","DOI":"10.1016\/j.ecoinf.2023.102282","article-title":"FirePred: A Hybrid Multi-Temporal Convolutional Neural Network Model for Wildfire Spread Prediction","volume":"78","author":"Marjani","year":"2023","journal-title":"Ecol. 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