{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T16:14:40Z","timestamp":1774541680471,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,5]],"date-time":"2021-08-05T00:00:00Z","timestamp":1628121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2020R1I1A3A04037483),"],"award-info":[{"award-number":["NRF-2020R1I1A3A04037483),"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this study, building extraction in aerial images was performed using csAG-HRNet by applying HRNet-v2 in combination with channel and spatial attention gates. HRNet-v2 consists of transition and fusion processes based on subnetworks according to various resolutions. The channel and spatial attention gates were applied in the network to efficiently learn important features. A channel attention gate assigns weights in accordance with the importance of each channel, and a spatial attention gate assigns weights in accordance with the importance of each pixel position for the entire channel. In csAG-HRNet, csAG modules consisting of a channel attention gate and a spatial attention gate were applied to each subnetwork of stage and fusion modules in the HRNet-v2 network. In experiments using two datasets, it was confirmed that csAG-HRNet could minimize false detections based on the shapes of large buildings and small nonbuilding objects compared to existing deep learning models.<\/jats:p>","DOI":"10.3390\/rs13163087","type":"journal-article","created":{"date-parts":[[2021,8,5]],"date-time":"2021-08-05T21:43:53Z","timestamp":1628199833000},"page":"3087","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":72,"title":["Semantic Segmentation of Urban Buildings Using a High-Resolution Network (HRNet) with Channel and Spatial Attention Gates"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2805-1750","authenticated-orcid":false,"given":"Seonkyeong","family":"Seong","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, Chungbuk National University, Cheongju 28644, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3967-6481","authenticated-orcid":false,"given":"Jaewan","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, Chungbuk National University, Cheongju 28644, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. 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