{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,11]],"date-time":"2026-10-11T03:44:01Z","timestamp":1791690241760,"version":"4.3.4"},"reference-count":39,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,4,19]],"date-time":"2025-04-19T00:00:00Z","timestamp":1745020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Fund of Institute of Seismology and Institute of Crustal Dynamics, China Earthquake Administration","award":["IS202226324"],"award-info":[{"award-number":["IS202226324"]}]},{"name":"Scientific Research Fund of Institute of Seismology and Institute of Crustal Dynamics, China Earthquake Administration","award":["2022HBJJ009"],"award-info":[{"award-number":["2022HBJJ009"]}]},{"name":"Scientific Research Fund of Institute of Seismology and Institute of Crustal Dynamics, China Earthquake Administration","award":["42477504"],"award-info":[{"award-number":["42477504"]}]},{"name":"Scientific Research Fund of Institute of Seismology and Institute of Crustal Dynamics, China Earthquake Administration","award":["42471486"],"award-info":[{"award-number":["42471486"]}]},{"name":"Research Foundation Project of Hubei Earthquake Agency","award":["IS202226324"],"award-info":[{"award-number":["IS202226324"]}]},{"name":"Research Foundation Project of Hubei Earthquake Agency","award":["2022HBJJ009"],"award-info":[{"award-number":["2022HBJJ009"]}]},{"name":"Research Foundation Project of Hubei Earthquake Agency","award":["42477504"],"award-info":[{"award-number":["42477504"]}]},{"name":"Research Foundation Project of Hubei Earthquake Agency","award":["42471486"],"award-info":[{"award-number":["42471486"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["IS202226324"],"award-info":[{"award-number":["IS202226324"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022HBJJ009"],"award-info":[{"award-number":["2022HBJJ009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42477504"],"award-info":[{"award-number":["42477504"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42471486"],"award-info":[{"award-number":["42471486"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["IS202226324"],"award-info":[{"award-number":["IS202226324"]}]},{"name":"National Natural Science Foundation of China","award":["2022HBJJ009"],"award-info":[{"award-number":["2022HBJJ009"]}]},{"name":"National Natural Science Foundation of China","award":["42477504"],"award-info":[{"award-number":["42477504"]}]},{"name":"National Natural Science Foundation of China","award":["42471486"],"award-info":[{"award-number":["42471486"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Deep-learning-based methods are crucial for building extraction from high-resolution remote sensing images, playing a key role in applications like natural disaster response, land resource management, and smart city development. However, extracting precise building from complex urban and rural environments remains challenging due to spectral variability and intricate background interference, particularly in densely packed and small buildings. To address these issues, we propose an enhanced U2-Net architecture, MFA-Net, which incorporates two key innovations: a Multidimensional Feature Adjustment (MFA) module that refines feature representations through Cascaded Channel, Spatial, and Multiscale Weighting Mechanisms and a Dynamic Fusion Loss function that enhances edge geometric fidelity. Evaluation on three datasets (Urban, Rural, and WHU) reveals that MFA-Net outperforms existing methods, with average improvements of 6% in F1-score and 7.3% in IoU and an average increase of 9.9% in training time. These advancements significantly improve edge delineation and the segmentation of dense building clusters, making MFA-Net especially beneficial for urban planning and land resource management.<\/jats:p>","DOI":"10.3390\/s25082589","type":"journal-article","created":{"date-parts":[[2025,4,20]],"date-time":"2025-04-20T20:24:16Z","timestamp":1745180656000},"page":"2589","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Building Segmentation in Urban and Rural Areas with MFA-Net: A Multidimensional Feature Adjustment Approach"],"prefix":"10.3390","volume":"25","author":[{"given":"Zijie","family":"Han","sequence":"first","affiliation":[{"name":"Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8267-7503","authenticated-orcid":false,"given":"Xue","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianteng","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihao","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2024.12.010","article-title":"Refined Change Detection in Heterogeneous Low-Resolution Remote Sensing Images for Disaster Emergency Response","volume":"220","author":"Wang","year":"2025","journal-title":"ISPRS J. 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