{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T09:14:53Z","timestamp":1775380493066,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2020,4,28]],"date-time":"2020-04-28T00:00:00Z","timestamp":1588032000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701446\uff0c41971356"],"award-info":[{"award-number":["41701446\uff0c41971356"]}],"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>Semantic segmentation of high-resolution remote sensing images plays an important role in applications for building extraction. However, the current algorithms have some semantic information extraction limitations, and these can lead to poor segmentation results. To extract buildings with high accuracy, we propose a multiloss neural network based on attention. The designed network, based on U-Net, can improve the sensitivity of the model by the attention block and suppress the background influence of irrelevant feature areas. To improve the ability of the model, a multiloss approach is proposed during training the network. The experimental results show that the proposed model offers great improvement over other state-of-the-art methods. For the public Inria Aerial Image Labeling dataset, the F1 score reached 76.96% and showed good performance on the Aerial Imagery for Roof Segmentation dataset.<\/jats:p>","DOI":"10.3390\/rs12091400","type":"journal-article","created":{"date-parts":[[2020,4,29]],"date-time":"2020-04-29T01:29:15Z","timestamp":1588123755000},"page":"1400","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":148,"title":["Building Extraction Based on U-Net with an Attention Block and Multiple Losses"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4097-4814","authenticated-orcid":false,"given":"Mingqiang","family":"Guo","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Heng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7421-4915","authenticated-orcid":false,"given":"Yongyang","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Ying","family":"Huang","sequence":"additional","affiliation":[{"name":"Wuhan Zondy Cyber Technology Co. Ltd., Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Rees, W.G. (2013). Physical Principles of Remote Sensing, Cambridge University Press.","DOI":"10.1017\/CBO9781139017411"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1929","DOI":"10.1080\/13658816.2017.1341632","article-title":"Quality assessment of building footprint data using a deep autoencoder network","volume":"31","author":"Xu","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, Y., Minh Nguyen, D., Deligiannis, N., Ding, W., and Munteanu, A. (2017). Hourglass-shapenetwork based semantic segmentation for high resolution aerial imagery. Remote Sens., 9.","DOI":"10.3390\/rs9060522"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, Y., Piramanayagam, S., Monteiro, S.T., and Saber, E. (2017, January 21\u201326). 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