{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T16:13:41Z","timestamp":1778948021718,"version":"3.51.4"},"reference-count":60,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T00:00:00Z","timestamp":1643414400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&amp;D Program of China","award":["2018YFD1100301"],"award-info":[{"award-number":["2018YFD1100301"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42001276"],"award-info":[{"award-number":["42001276"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LQ19D010006"],"award-info":[{"award-number":["LQ19D010006"]}]},{"name":"the Strategic Priority Research Program of Chinese Academy of Sciences","award":["XDA 20030302"],"award-info":[{"award-number":["XDA 20030302"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep convolutional neural network (DCNN)-based methods have shown great improvements in building extraction from high spatial resolution remote sensing images. In this paper, we propose a postprocessing method based on DCNNs for building extraction. Specifically, building regions and boundaries are learned simultaneously or separately by DCNNs. The predicted building regions and boundaries are then combined by the postprocessing method to produce the final building regions. In addition, we introduce a manually labeled dataset based on high spatial resolution images for building detection, the XIHU building dataset. This dataset is then used in the experiments to evaluate our methods. Besides the WHU building dataset, East Asia (WHUEA) is also included. Results demonstrate that our method that combines the results of DeepLab and BDCN shows the best performance on the XIHU building dataset, which achieves 0.78% and 23.30% F1 scores, and 1.13% and 28.45% intersection-over-union (IoU) improvements compared with DeepLab and BDCN, respectively. Additionally, our method that combines the results of Mask R-CNN and DexiNed performs best on the WHUEA dataset. Moreover, our methods outperform the state-of-the-art multitask learning network, PMNet, on both XIHU and WHUEA datasets, which indicates that the overall performance can be improved although building regions and boundaries are learned in the training stage.<\/jats:p>","DOI":"10.3390\/rs14030647","type":"journal-article","created":{"date-parts":[[2022,1,30]],"date-time":"2022-01-30T00:12:56Z","timestamp":1643501576000},"page":"647","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Postprocessing Method Based on Regions and Boundaries Using Convolutional Neural Networks and a New Dataset for Building Extraction"],"prefix":"10.3390","volume":"14","author":[{"given":"Haiping","family":"Yang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meixia","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1269-9045","authenticated-orcid":false,"given":"Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Dong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101655","DOI":"10.1016\/j.compenvurbsys.2021.101655","article-title":"Impacts of tree and building shades on the urban heat island: Combining remote sensing, 3D digital city and spatial regression approaches","volume":"88","author":"Park","year":"2021","journal-title":"Comput. 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