{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T15:35:01Z","timestamp":1781796901963,"version":"3.54.5"},"reference-count":66,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["41971387"],"award-info":[{"award-number":["41971387"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["2020JM-430"],"award-info":[{"award-number":["2020JM-430"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Shaanxi Province, China","award":["41971387"],"award-info":[{"award-number":["41971387"]}]},{"name":"Natural Science Foundation of Shaanxi Province, China","award":["2020JM-430"],"award-info":[{"award-number":["2020JM-430"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The novel concept of local climate zones (LCZs) provides a consistent classification framework for studies of the urban thermal environment. However, the development of urban climate science is severely hampered by the lack of high-resolution data to map LCZs. Using Gaofen-6 and Sentinel-1\/2 as data sources, this study designed four schemes using convolutional neural network (CNN) and random forest (RF) classifiers, respectively, to demonstrate the potential of high-resolution images in LCZ mapping and evaluate the optimal combination of different data sources and classifiers. The results showed that the combination of GF-6 and CNN (S3) was considered the best LCZ classification scheme for urban areas, with OA and kappa coefficients of 85.9% and 0.842, respectively. The accuracy of urban building categories is above 80%, and the F1 score for each category is the highest, except for LCZ1 and LCZ5, where there is a small amount of confusion. The Sentinel-1\/2-based RF classifier (S2) was second only to S3 and superior to the combination of GF-6 and random forest (S1), with OA and kappa coefficients of 64.4% and 0.612, respectively. The Sentinel-1\/2 and CNN (S4) combination has the worst classification result, with an OA of only 39.9%. The LCZ classification map based on S3 shows that the urban building categories in Xi\u2019an are mainly distributed within the second ring, while heavy industrial buildings have started to appear in the third ring. The urban periphery is mainly vegetated and bare land. In conclusion, CNN has the best application effect in the LCZ mapping task of high-resolution remote sensing images. In contrast, the random forest algorithm has better robustness in the band-abundant Sentinel data.<\/jats:p>","DOI":"10.3390\/s22176407","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T01:37:55Z","timestamp":1661823475000},"page":"6407","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Mapping Local Climate Zones in the Urban Environment: The Optimal Combination of Data Source and Classifier"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8575-5653","authenticated-orcid":false,"given":"Siying","family":"Cui","sequence":"first","affiliation":[{"name":"College of Urban and Environmental Science, Northwest University, Xi\u2019an 710127, China"},{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi\u2019an 710127, China"},{"name":"Shaanxi Xi\u2019an Urban Forest Ecosystem Research Station, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuhong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Science, Northwest University, Xi\u2019an 710127, China"},{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi\u2019an 710127, China"},{"name":"Shaanxi Xi\u2019an Urban Forest Ecosystem Research Station, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xia","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Science, Northwest University, Xi\u2019an 710127, China"},{"name":"Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi\u2019an 710127, China"},{"name":"Shaanxi Xi\u2019an Urban Forest Ecosystem Research Station, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifa","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Science, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqi","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Science, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0145-0196","authenticated-orcid":false,"given":"Zihao","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Science, Northwest University, Xi\u2019an 710127, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1126\/science.1150195","article-title":"Global Change and the Ecology of Cities","volume":"319","author":"Grimm","year":"2008","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.rse.2014.05.017","article-title":"Surface Urban Heat Island in China\u2019s 32 Major Cities: Spatial Patterns and Drivers","volume":"152","author":"Zhou","year":"2014","journal-title":"Remote Sens. 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