{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T17:00:12Z","timestamp":1783184412998,"version":"3.54.6"},"reference-count":69,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T00:00:00Z","timestamp":1593993600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Nature Science Foundation of China","award":["61731009 and 41301472"],"award-info":[{"award-number":["61731009 and 41301472"]}]},{"name":"the Science and Technology Commission of Shanghai Municipality","award":["19511120600"],"award-info":[{"award-number":["19511120600"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning methods have been used to extract buildings from remote sensing images and have achieved state-of-the-art performance. Most previous work has emphasized the multi-scale fusion of features or the enhancement of more receptive fields to achieve global features rather than focusing on low-level details such as the edges. In this work, we propose a novel end-to-end edge-aware network, the EANet, and an edge-aware loss for getting accurate buildings from aerial images. Specifically, the architecture is composed of image segmentation networks and edge perception networks that, respectively, take charge of building prediction and edge investigation. The International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam segmentation benchmark and the Wuhan University (WHU) building benchmark were used to evaluate our approach, which, respectively, was found to achieve 90.19% and 93.33% intersection-over-union and top performance without using additional datasets, data augmentation, and post-processing. The EANet is effective in extracting buildings from aerial images, which shows that the quality of image segmentation can be improved by focusing on edge details.<\/jats:p>","DOI":"10.3390\/rs12132161","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T11:07:42Z","timestamp":1594033662000},"page":"2161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["EANet: Edge-Aware Network for the Extraction of Buildings from Aerial Images"],"prefix":"10.3390","volume":"12","author":[{"given":"Guang","family":"Yang","sequence":"first","affiliation":[{"name":"The Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China"},{"name":"School of Computer Science and Technology, East China Normal University, Shanghai 200062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3041-643X","authenticated-orcid":false,"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China"},{"name":"School of Computer Science and Technology, East China Normal University, Shanghai 200062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guixu","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China"},{"name":"School of Computer Science and Technology, East China Normal University, Shanghai 200062, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,6]]},"reference":[{"key":"ref_1","first-page":"150","article-title":"Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours","volume":"12","author":"Ahmadi","year":"2010","journal-title":"Int. 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