{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T20:36:32Z","timestamp":1781901392832,"version":"3.54.5"},"reference-count":67,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"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":["41401093"],"award-info":[{"award-number":["41401093"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41401397"],"award-info":[{"award-number":["41401397"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["KYCX20_2374"],"award-info":[{"award-number":["KYCX20_2374"]}]},{"name":"the Postgraduate Research and Practice Innovation Program of Jiangsu Province","award":["KYCX20_2369"],"award-info":[{"award-number":["KYCX20_2369"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Timely and accurate information on rural settlements is essential for rural development planning. Remote sensing has become an important means for accurately mapping large scale rural settlements. Nevertheless, numerous difficulties remain in accurate and efficient rural settlement extraction. In this study, by combining multi-dimensional features derived from Sentinel-1\/2 images, Visible Infrared Imaging Radiometer Suite supporting a Day-Night Band (VIIRS-DNB) dataset, and Digital Elevation Model (DEM) data using the Google Earth Engine (GEE) platform, we proposed an efficient framework with good transferability for mapping rural settlements in the Yangtze River Delta. To avoid the time-consuming selection of a large number of training samples in the whole study area, we employed four random forest models obtained from the training samples in respective training municipal districts in four different regions to classify other municipal districts in their corresponding region. We found that different features play diverse vital roles in the extraction of rural settlements in various regions. Compared to results only using optical data, accuracies obtained by the proposed method were significantly improved. The average user\u2019s accuracy, producer\u2019s accuracy, overall accuracy, and Kappa coefficient increased by 16.75%, 17.75%, 11.50%, and 14.50% in the four training municipal administrative areas, respectively. The overall accuracy and Kappa coefficient were 96% and 0.84, respectively. By contrast, our classification results are superior to other public datasets. The final mapping results provided a detailed spatial distribution of the rural settlements in the Yangtze River Delta and revealed that the total area of rural settlements is approximately 32,121.1 km2, accounting for 17.41% of the total area. The high-density rural settlements are mainly distributed in the Northern Plain and East Coast, while the low-density rural settlements are located in the Central Hills and Southern Mountain.<\/jats:p>","DOI":"10.3390\/rs12172832","type":"journal-article","created":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T08:53:43Z","timestamp":1598950423000},"page":"2832","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Mapping 10-m Resolution Rural Settlements Using Multi-Source Remote Sensing Datasets with the Google Earth Engine Platform"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7522-6168","authenticated-orcid":false,"given":"Hanyu","family":"Ji","sequence":"first","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3793-1581","authenticated-orcid":false,"given":"Xing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinchun","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1146\/annurev.es.23.110192.000351","article-title":"Human population growth and global land-use\/cover change","volume":"23","author":"Meyer","year":"1992","journal-title":"Annu. 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