{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T14:35:23Z","timestamp":1775226923713,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,2,26]],"date-time":"2020-02-26T00:00:00Z","timestamp":1582675200000},"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":["61671091"],"award-info":[{"award-number":["61671091"]}],"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>In recent years, the increase of satellites and UAV (unmanned aerial vehicles) has multiplied the amount of remote sensing data available to people, but only a small part of the remote sensing data has been properly used; problems such as land planning, disaster management and resource monitoring still need to be solved. Buildings in remote sensing images have obvious positioning characteristics; thus, the detection of buildings can not only help the mapping and automatic updating of geographic information systems but also have guiding significance for the detection of other types of ground objects in remote sensing images. Aiming at the deficiency of traditional building remote sensing detection, an improved Faster R-CNN (region-based Convolutional Neural Network) algorithm was proposed in this paper, which adopts DRNet (Dense Residual Network) and RoI (Region of Interest) Align to utilize texture information and to solve the region mismatch problems. The experimental results showed that this method could reach 82.1% mAP (mean average precision) for the detection of landmark buildings, and the prediction box of building coordinates was relatively accurate, which improves the building detection results. Moreover, the recognition of buildings in a complex environment was also excellent.<\/jats:p>","DOI":"10.3390\/rs12050762","type":"journal-article","created":{"date-parts":[[2020,2,27]],"date-time":"2020-02-27T03:21:16Z","timestamp":1582773676000},"page":"762","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["An Optimized Faster R-CNN Method Based on DRNet and RoI Align for Building Detection in Remote Sensing Images"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5612-0675","authenticated-orcid":false,"given":"Tong","family":"Bai","sequence":"first","affiliation":[{"name":"Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Yu","family":"Pang","sequence":"additional","affiliation":[{"name":"Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2560-8728","authenticated-orcid":false,"given":"Junchao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Shantou University, Shantou 515063, China"}]},{"given":"Kaining","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Shantou University, Shantou 515063, China"}]},{"given":"Jiasai","family":"Luo","sequence":"additional","affiliation":[{"name":"Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Huiqian","family":"Wang","sequence":"additional","affiliation":[{"name":"Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Jinzhao","family":"Lin","sequence":"additional","affiliation":[{"name":"Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}]},{"given":"Jun","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Software Application Technology, Guangzhou &amp; Chinese Academy of Sciences, Guangzhou 511458, China"}]},{"given":"Hui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Software Application Technology, Guangzhou &amp; Chinese Academy of Sciences, Guangzhou 511458, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. 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