{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T14:18:00Z","timestamp":1777126680072,"version":"3.51.4"},"reference-count":27,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Open Fund of Key Laboratory of Urban Land Resource Monitoring and Simulation, Ministry of Land and Resource","award":["KF-2018-03-047"],"award-info":[{"award-number":["KF-2018-03-047"]}]},{"name":"Hunan Provincial Natural Science Foundation of China","award":["2018JJ3637"],"award-info":[{"award-number":["2018JJ3637"]}]},{"name":"Hunan Postgraduate Research and Innovation Project","award":["CX20190219"],"award-info":[{"award-number":["CX20190219"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>This paper presents an automatic building extraction method which utilizes a photogrammetric digital surface model (DSM) and digital orthophoto map (DOM) with the help of historical digital line graphic (DLG) data. To reduce the need for manual labeling, the initial labels were automatically obtained from historical DLGs. Nonetheless, a proportion of these labels are incorrect due to changes (e.g., new constructions, demolished buildings). To select clean samples, an iterative method using random forest (RF) classifier was proposed in order to remove some possible incorrect labels. To get effective features, deep features extracted from normalized DSM (nDSM) and DOM using the pre-trained fully convolutional networks (FCN) were combined. To control the computation cost and alleviate the burden of redundancy, the principal component analysis (PCA) algorithm was applied to reduce the feature dimensions. Three data sets in two areas were employed with evaluation in two aspects. In these data sets, three DLGs with 15%, 65%, and 25% of noise were applied. The results demonstrate the proposed method could effectively select clean samples, and maintain acceptable quality of extracted results in both pixel-based and object-based evaluations.<\/jats:p>","DOI":"10.3390\/ijgi9010018","type":"journal-article","created":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T04:43:03Z","timestamp":1578026583000},"page":"18","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Extracting Building Areas from Photogrammetric DSM and DOM by Automatically Selecting Training Samples from Historical DLG Data"],"prefix":"10.3390","volume":"9","author":[{"given":"Siyang","family":"Chen","sequence":"first","affiliation":[{"name":"School of Geoscience and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2779-2015","authenticated-orcid":false,"given":"Yunsheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geoscience and Info-Physics, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Nie","sequence":"additional","affiliation":[{"name":"Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Land and Resources, Shenzhen 518034, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Li","sequence":"additional","affiliation":[{"name":"Research Institute of Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 516080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weixi","family":"Wang","sequence":"additional","affiliation":[{"name":"Research Institute of Smart Cities, School of Architecture and Urban Planning, Shenzhen University, Shenzhen 516080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.isprsjprs.2017.06.005","article-title":"Automatic building extraction from LiDAR data fusion of point and grid-based features","volume":"130","author":"Du","year":"2017","journal-title":"ISPRS J. 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