{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T15:24:45Z","timestamp":1784042685559,"version":"3.55.0"},"reference-count":76,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,3,28]],"date-time":"2020-03-28T00:00:00Z","timestamp":1585353600000},"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":["41671369"],"award-info":[{"award-number":["41671369"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2017YFB0503600"],"award-info":[{"award-number":["2017YFB0503600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2017- YX001"],"award-info":[{"award-number":["2017- YX001"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The urban functional zone, as a special fundamental unit of the city, helps to understand the complex interaction between human space activities and environmental changes. Based on the recognition of physical and social semantics of buildings, combining remote sensing data and social sensing data is an effective way to quickly and accurately comprehend urban functional zone patterns. From the object level, this paper proposes a novel object-wise recognition strategy based on very high spatial resolution images (VHSRI) and social sensing data. First, buildings are extracted according to the physical semantics of objects; second, remote sensing and point of interest (POI) data are combined to comprehend the spatial distribution and functional semantics in the social function context; finally, urban functional zones are recognized and determined by building with physical and social functional semantics. When it comes to building geometrical information extraction, this paper, given the importance of building boundary information, introduces the deeper edge feature map (DEFM) into the segmentation and classification, and improves the result of building boundary recognition. Given the difficulty in understanding deeper semantics and spatial information and the limitation of traditional convolutional neural network (CNN) models in feature extraction, we propose the Deeper-Feature Convolutional Neural Network (DFCNN), which is able to extract more and deeper features for building semantic recognition. Experimental results conducted on a Google Earth image of Shenzhen City show that the proposed method and model are able to effectively, quickly, and accurately recognize urban functional zones by combining building physical semantics and social functional semantics, and are able to ensure the accuracy of urban functional zone recognition.<\/jats:p>","DOI":"10.3390\/rs12071088","type":"journal-article","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T03:44:13Z","timestamp":1585712653000},"page":"1088","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Hanqing","family":"Bao","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3422-7399","authenticated-orcid":false,"given":"Dongping","family":"Ming","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian, Beijing 100083, China"},{"name":"Polytechnic Center for Natural Resources Big-data, Ministry of Natural Resources of China, Beijing 100036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kui","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keqi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shigao","family":"Du","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences (Beijing), 29 Xueyuan Road, Haidian, Beijing 100083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,28]]},"reference":[{"key":"ref_1","first-page":"15","article-title":"Unsupervised change detection in VHR remote sensing imagery\u2014An object-based clustering approach in a dynamic urban environment","volume":"54","author":"Leichtle","year":"2017","journal-title":"Int. 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