{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:07:55Z","timestamp":1792789675070,"version":"4.5.0"},"reference-count":45,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Building extraction underpins land-use assessment, urban planning, and disaster mitigation, yet dense urban scenes still cause missed small objects, target adhesion, and ragged contours. We present High-Resolution-Mamba (HR-Mamba), a high-resolution semantic segmentation network that augments a High-Resolution Network (HRNet) parallel backbone with edge-aware and sequence-state modeling. A Canny-enhanced, median-filtered stem stabilizes boundaries under noise; Involution-based residual blocks capture position-specific local geometry; and a Mamba-based State Space Models (Mamba-SSM) global branch captures cross-scale long-range dependencies with linear complexity. Training uses a composite loss of binary cross entropy (BCE), Dice loss, and Boundary loss, with weights selected by joint grid search. We further design a feature-driven adaptive post-processing pipeline that includes geometric feature analysis, multi-strategy simplification, multi-directional regularization, and topological consistency verification to produce regular, smooth, engineering-ready building outlines. On dense urban imagery, HR-Mamba improves F1-score from 80.95% to 83.93%, an absolute increase of 2.98% relative to HRNet. We conclude that HR-Mamba jointly enhances detail fidelity and global consistency and offers a generalizable route for high-resolution building extraction in remote sensing.<\/jats:p>","DOI":"10.3390\/s26020352","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T08:42:58Z","timestamp":1767688978000},"page":"352","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["HR-Mamba: Building Footprint Segmentation with Geometry-Driven Boundary Regularization"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6302-9056","authenticated-orcid":false,"given":"Buyu","family":"Su","sequence":"first","affiliation":[{"name":"National Key Laboratory of Uranium Resource Exploration-Mining and Nuclear Remote Sensing, Beijing Research Institute of Uranium Geology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Defei","family":"Yin","sequence":"additional","affiliation":[{"name":"Science and Technology on Near-Surface Detection Laboratory, The Fifth Research Institute of Wuxi, Wuxi 214035, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4264-6021","authenticated-orcid":false,"given":"Piyuan","family":"Yi","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Uranium Resource Exploration-Mining and Nuclear Remote Sensing, Beijing Research Institute of Uranium Geology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1718-390X","authenticated-orcid":false,"given":"Wenhuan","family":"Wu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Uranium Resource Exploration-Mining and Nuclear Remote Sensing, Beijing Research Institute of Uranium Geology, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8339-293X","authenticated-orcid":false,"given":"Junjian","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Desert Meteorology, China Meteorological Administration\/National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang\/Taklimakan Desert Meteorology Field Experiment Station of CMA, Urumqi 830002, China"},{"name":"Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9613-1598","authenticated-orcid":false,"given":"Fan","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Desert Meteorology, China Meteorological Administration\/National Observation and Research Station of Desert Meteorology, Taklimakan Desert of Xinjiang\/Taklimakan Desert Meteorology Field Experiment Station of CMA, Urumqi 830002, China"},{"name":"Xinjiang Key Laboratory of Desert Meteorology and Sandstorm, Urumqi 830002, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9465-822X","authenticated-orcid":false,"given":"Haowei","family":"Mu","sequence":"additional","affiliation":[{"name":"School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyi","family":"Xiong","sequence":"additional","affiliation":[{"name":"Urban Safety Engineering, Beijing Polytechnic College, Beijing 100042, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1109\/JSTARS.2021.3139017","article-title":"CGSANet: A Contour-Guided and Local Structure-Aware Encoder\u2013Decoder Network for Accurate Building Extraction From Very High-Resolution Remote Sensing Imagery","volume":"15","author":"Chen","year":"2022","journal-title":"IEEE J. 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