{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:56:46Z","timestamp":1760147806733,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T00:00:00Z","timestamp":1677801600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Fund of Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources","award":["MEMI-2021-2022-09"],"award-info":[{"award-number":["MEMI-2021-2022-09"]}]},{"name":"Interdisciplinary Cultivation Fund under Project SWJTU","award":["MEMI-2021-2022-09"],"award-info":[{"award-number":["MEMI-2021-2022-09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Weakly supervised semantic segmentation (WSSS) methods, utilizing only image-level annotations, are gaining popularity for automated building extraction due to their advantages in eliminating the need for costly and time-consuming pixel-level labeling. Class activation maps (CAMs) are crucial for weakly supervised methods to generate pseudo-pixel-level labels for training networks in semantic segmentation. However, CAMs only activate the most discriminative regions, leading to inaccurate and incomplete results. To alleviate this, we propose a scale-invariant multi-level context aggregation network to improve the quality of CAMs in terms of fineness and completeness. The proposed method has integrated two novel modules into a Siamese network: (a) a self-attentive multi-level context aggregation module that generates and attentively aggregates multi-level CAMs to create fine-structured CAMs and (b) a scale-invariant optimization module that cooperates with mutual learning and coarse-to-fine optimization to improve the completeness of CAMs. The results of the experiments on two open building datasets demonstrate that our method achieves new state-of-the-art building extraction results using only image-level labels, producing more complete and accurate CAMs with an IoU of 0.6339 on the WHU dataset and 0.5887 on the Chicago dataset, respectively.<\/jats:p>","DOI":"10.3390\/rs15051432","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T01:35:30Z","timestamp":1678066530000},"page":"1432","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Scale-Invariant Multi-Level Context Aggregation Network for Weakly Supervised Building Extraction"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2744-7059","authenticated-orcid":false,"given":"Jicheng","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Land Resources Evaluation and Monitoring in Southwest China of Ministry of Education, Sichuan Normal University, Chengdu 610068, China"},{"name":"Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Yan","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4638-3329","authenticated-orcid":false,"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8907-2603","authenticated-orcid":false,"given":"Tian","family":"Lan","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6700-2241","authenticated-orcid":false,"given":"Xunqiang","family":"Gong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1507-323X","authenticated-orcid":false,"given":"Zhilin","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1080\/01431160512331316469","article-title":"Remote Sensing of Urban Areas","volume":"26","author":"Maktav","year":"2005","journal-title":"Int. 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