{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T16:15:24Z","timestamp":1775751324538,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T00:00:00Z","timestamp":1574812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"From 0 to 1 Original Innovative Project of the Chinese Academy of Sciences Frontier Science Research Program","award":["ZDBS-LY-JSC036"],"award-info":[{"award-number":["ZDBS-LY-JSC036"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61901439"],"award-info":[{"award-number":["61901439"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Automatic building extraction from high-resolution remote sensing images has many practical applications, such as urban planning and supervision. However, fine details and various scales of building structures in high-resolution images bring new challenges to building extraction. An increasing number of neural network-based models have been proposed to handle these issues, while they are not efficient enough, and still suffer from the error ground truth labels. To this end, we propose an efficient end-to-end model, EU-Net, in this paper. We first design the dense spatial pyramid pooling (DSPP) to extract dense and multi-scale features simultaneously, which facilitate the extraction of buildings at all scales. Then, the focal loss is used in reverse to suppress the impact of the error labels in ground truth, making the training stage more stable. To assess the universality of the proposed model, we tested it on three public aerial remote sensing datasets: WHU aerial imagery dataset, Massachusetts buildings dataset, and Inria aerial image labeling dataset. Experimental results show that the proposed EU-Net is superior to the state-of-the-art models of all three datasets and increases the prediction efficiency by two to four times.<\/jats:p>","DOI":"10.3390\/rs11232813","type":"journal-article","created":{"date-parts":[[2019,11,27]],"date-time":"2019-11-27T11:07:00Z","timestamp":1574852820000},"page":"2813","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":83,"title":["EU-Net: An Efficient Fully Convolutional Network for Building Extraction from Optical Remote Sensing Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0445-1568","authenticated-orcid":false,"given":"Wenchao","family":"Kang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Huairou District, Beijing 101408, China"},{"name":"Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2063-9816","authenticated-orcid":false,"given":"Yuming","family":"Xiang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Huairou District, Beijing 101408, China"},{"name":"Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6494-3639","authenticated-orcid":false,"given":"Feng","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}]},{"given":"Hongjian","family":"You","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Huairou District, Beijing 101408, China"},{"name":"Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2017.12.007","article-title":"Semantic labeling in very high resolution images via a self-cascaded convolutional neural network","volume":"145","author":"Liu","year":"2018","journal-title":"ISPRS J. 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