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It plays a significant role in urban planning, land management, and disaster monitoring. Deep-learning methods have shown advantages in building extraction, but they still face challenges such as variations in building types, object occlusions, and complex backgrounds. To address these issues, SDSNet, a deep convolutional network that incorporates global multi-scale feature extraction and cross-level feature fusion, is proposed. SDSNet consists of three modules: semantic information extraction (SIE), multi-level merge (MLM), and semantic information fusion (SIF). The SIE module extracts contextual information and improves recognition of multi-scale buildings. The MLM module filters irrelevant details guided by high-level semantic information, aiding in the restoration of edge details for buildings. The SIF module combines filtered detail information with extracted semantic information for refined building extraction. A series of experiments conducted on two distinct public datasets for building extraction consistently demonstrate that SDSNet outperforms the state-of-the-art deep-learning models for building extraction tasks. On the WHU building dataset, the overall accuracy (OA) and intersection over union (IoU) achieved impressive scores of 98.86% and 90.17%, respectively. Meanwhile, on the Massachusetts dataset, SDSNet achieved OA and IoU scores of 94.05% and 71.6%, respectively. SDSNet exhibits a unique advantage in recovering fine details along building edges, enabling automated and intelligent building extraction. This capability effectively supports urban planning, resource management, and disaster monitoring.<\/jats:p>","DOI":"10.3390\/rs16010169","type":"journal-article","created":{"date-parts":[[2023,12,31]],"date-time":"2023-12-31T04:51:51Z","timestamp":1703998311000},"page":"169","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["SDSNet: Building Extraction in High-Resolution Remote Sensing Images Using a Deep Convolutional Network with Cross-Layer Feature Information Interaction Filtering"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-2291-1812","authenticated-orcid":false,"given":"Xudong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0228-7177","authenticated-orcid":false,"given":"Mingliang","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430078, China"},{"name":"Xining Center of Natural Resources Comprehensive Survey, China Geological Survey, Xining 810000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"He","sequence":"additional","affiliation":[{"name":"Hubei Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"},{"name":"Hubei Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6424-0623","authenticated-orcid":false,"given":"Yusen","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430078, China"},{"name":"Hubei Key Laboratory of Geological Survey and Evaluation of Ministry of Education, China University of Geosciences, Wuhan 430078, China"},{"name":"Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,31]]},"reference":[{"key":"ref_1","first-page":"51","article-title":"Building modern land administration systems in developed economies","volume":"50","author":"Enemark","year":"2005","journal-title":"Surveyor"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. 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