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This paper proposes a building extraction model called BPKG-SegFormer (Building Prior Knowledge Guided SegFormer) that combines prior knowledge of buildings with data-driven methods. This model constructs a building feature attention module and utilizes the multi-task loss function to optimize the extraction of buildings. Experimental results show that on the WHU building dataset, the proposed model outperforms UNet, Deeplabv3\u2009+\u2009, and SegFormer models with OA, P, R, and MIoU of 96.63%, 95.94%, 94.76%, and 90.6%, respectively. The BPKG-SegFormer model extracts buildings with more regular shapes and flatter edges, reducing internal voids and increasing the number of correctly detected buildings.<\/jats:p>","DOI":"10.1007\/s44212-024-00038-8","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T02:02:03Z","timestamp":1708912923000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A prior knowledge guided deep learning method for building extraction from high-resolution remote sensing images"],"prefix":"10.1007","volume":"3","author":[{"given":"Ming","family":"Hao","sequence":"first","affiliation":[]},{"given":"Shilin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Huijing","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Hua","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Nanshan","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"key":"38_CR1","first-page":"1134","volume":"24","author":"K Chen","year":"2020","unstructured":"Chen, K., Gao, X., & Yan, M. 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