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Unfortunately, due to the high acquisition costs and infrequent availability of high-resolution imagery, low-resolution images are more practical for large-scale mapping or change tracking of buildings. However, extracting buildings from low-resolution images is a challenging task. Compared with high-resolution images, low-resolution images pose two critical challenges in terms of building segmentation: the effects of fuzzy boundary details on buildings and the lack of local textures. In this study, we propose a sparse geometric feature attention network (SGFANet) based on multi-level feature fusion to address the aforementioned issues. From the perspective of the fuzzy effect, SGFANet enhances the representative boundary features by calculating the point-wise affinity of the selected feature points in a top-down manner. From the perspective of lacking local textures, we convert the top-down propagation from local to non-local by introducing the grounding transformer harvesting the global attention of the input image. SGFANet outperforms competing baselines on remote-sensing images collected worldwide and multiple sensors at 4 and 10 m resolution, thereby, improving the IoU by at least 0.66%. Notably, our method is robust and generalizable, which makes it useful for extending the accessibility and scalability of building dynamic tracking across developing areas (e.g., the Xiong\u2019an New Area in China) by using low-resolution images.<\/jats:p>","DOI":"10.3390\/rs15071741","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T02:34:54Z","timestamp":1679625294000},"page":"1741","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Learning Sparse Geometric Features for Building Segmentation from Low-Resolution Remote-Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2898-0023","authenticated-orcid":false,"given":"Zeping","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4091-0175","authenticated-orcid":false,"given":"Hong","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China"},{"name":"State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108235","DOI":"10.1016\/j.buildenv.2021.108235","article-title":"Modelling building energy use at urban scale: A review on their account for the urban environment","volume":"205","author":"Wong","year":"2021","journal-title":"Build. 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