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Urban climate problems have become increasingly prominent, especially with regard to the intensification of the urban heat island (UHI) effect. The local climate zone (LCZ) is a new quantitative method for analyzing urban climate that is based on the kind of urban surface and can effectively deal with the problem of the hazy distinction between urban and rural areas in UHI effect research. LCZs are widely used in regional climate modeling, urban planning, and thermal comfort surveys. Existing large-scale LCZ classification methods usually use visual features of optical images, such as spectral and textural features. There are many problems with hyperspectral LCZ extraction over large areas. LCZ is an integrated concept that includes features of the geography, society, and economy. Consequently, it makes sense to consider the characteristics of human activity and the visual features of the images to interpret them accurately. ALOS_DEM data can depict the city\u2019s physical characteristics; however, images of nighttime lights are crucial indicators of human activity. These three datasets can be used in combination to portray the urban environment. Therefore, this study proposes a method for fusing daytime and nighttime data for LCZ mapping, i.e., fusing daytime Zhuhai-1 hyperspectral images and their derived feature indices, ALOS_DEM data, and nighttime light data from Luojia-1. By combining daytime and nighttime information, the proposed approach captures the temporal dynamics of urban areas, providing a more complete representation of their characteristics. The integration of the data allows for a more refined identification and characterization of urban land cover. It comprehensively integrates daytime and nighttime data, exploits synergistic information from multiple sources, and provides higher accuracy and resolution for LCZ mapping. First, we extracted various features, namely spectral, red-edge, and textural features, from the Zhuhai-1 images, ALOS_DEM data, and nighttime light data from Luojia-1. Random forest (RF) and XGBoost classifiers were used, and the average impurity reduction method was employed to assess the significance of the variables. All the input variables were optimized to select the best combination of variables. The results from a study of the 5th ring road area of Beijing, China, revealed that the technique achieved LCZ mapping with good precision, with a total accuracy of 87.34%. In addition, to examine and contrast the effects of various feature indices on the LCZ classification accuracy, feature combination methods were used. The results of the study showed that the accuracies of LCZ classification in terms of spectral and textural were improved by 2.33% and 2.19% using the RF classifier, respectively. The radiation brightness value (RBV) (GI value = 0.0212) attained the classification\u2019s highest variable importance value; the DEM also produced a high GI value (0.0159), indicating that night lighting and landform features strongly influence LCZ classification.<\/jats:p>","DOI":"10.3390\/rs15133351","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:49:27Z","timestamp":1688345367000},"page":"3351","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Local Climate Zone Classification Using Daytime Zhuhai-1 Hyperspectral Imagery and Nighttime Light Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Ying","family":"Liang","sequence":"first","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"},{"name":"State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM, Beijing 100036, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0439-9915","authenticated-orcid":false,"given":"Wen","family":"Song","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"},{"name":"Key Laboratory of Urban Spatial Information, Ministry of Natural Resources, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9164-5805","authenticated-orcid":false,"given":"Shisong","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"},{"name":"Key Laboratory of Urban Spatial Information, Ministry of Natural Resources, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyi","family":"Du","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"},{"name":"Key Laboratory of Urban Spatial Information, Ministry of Natural Resources, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1080\/15481603.2022.2100100","article-title":"Seasonal and diurnal surface urban heat islands in China: An investigation of driving factors with three-dimensional urban morphological parameters","volume":"59","author":"Cao","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1080\/17538947.2020.1813210","article-title":"Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data","volume":"14","author":"Hashemi","year":"2021","journal-title":"Int. J. Digit. Earth"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2097","DOI":"10.30638\/eemj.2017.217","article-title":"Overview of Urban Heat Island (UHI) phenomenon towards human thermal comfort","volume":"16","author":"Lee","year":"2017","journal-title":"Environ. Eng. Manag. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1002\/joc.2141","article-title":"A systematic review and scientific critique of methodology in modern urban heat island literature","volume":"31","author":"Stewart","year":"2011","journal-title":"Int. J. Climatol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/S0034-4257(03)00079-8","article-title":"Thermal remote sensing of urban climates","volume":"86","author":"Voogt","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_6","first-page":"45","article-title":"Urban Heat Island Effect against the Background of Global Warming and Urbanization","volume":"6","author":"Li","year":"2012","journal-title":"Prog. Meteorol. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1002\/qj.49708435910","article-title":"On the frequency of snowfall in metropolitan England","volume":"84","author":"Manley","year":"1958","journal-title":"Q. J. R. Meteorolog. Soc."},{"key":"ref_8","unstructured":"Mills, G., Bechtel, B., Ching, J., See, L., Feddema, J., Foley, M., Alexander, P., and O\u2019Connor, M. (2015, January 20\u201324). An Introduction to the WUDAPT project. Proceedings of the 9th International Conference on Urban Climate (ICUC9), Toulouse, France."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1175\/BAMS-D-11-00019.1","article-title":"Local climate zones for urban temperature studies","volume":"93","author":"Stewart","year":"2012","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.14358\/PERS.70.9.1043","article-title":"Urban land-cover change analysis in Central Puget Sound","volume":"70","author":"Alberti","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.landurbplan.2013.08.011","article-title":"A spatio-temporal view of historical growth in Phoenix, Arizona, USA","volume":"121","author":"Kane","year":"2014","journal-title":"Landsc. Urban Plann."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1080\/13658816.2016.1244608","article-title":"Sensing spatial distribution of urban land use by integrating points-of-interest and Google Word2Vec model","volume":"31","author":"Yao","year":"2017","journal-title":"Int. J. Geog. Inf. Sci."},{"key":"ref_13","unstructured":"Johnston, R.B. (2016). Arsenic Research and Global Sustainability\u2014Proceedings of the 6th International Congress on Arsenic in the Environment, AS 2016, Stockholm, Sweden, 19\u201323 June 2016, CRC Press."},{"key":"ref_14","first-page":"18043","article-title":"Using the LCZ framework for change detection and urban growth monitoring","volume":"19","author":"Danylo","year":"2017","journal-title":"EGU Gen. Assem. Conf. Abstr."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bechtel, B., Conrad, O., Tamminga, M., Verdonck, M.L., Van Coillie, F., Tuia, D., Demuzere, M., See, L., Lopes, P., and Fonte, C.C. (2017, January 6\u20138). Beyond the urban mask: Local climate zones as a generic descriptor of urban areas\u2014Potential and recent developments. Proceedings of the 2017 Joint Urban Remote Sensing Event (JURSE), Dubai, United Arab Emirates.","DOI":"10.1109\/JURSE.2017.7924557"},{"key":"ref_16","unstructured":"Ching, J., Mills, G., See, L., Bechtel, B., Feddema, J., Stewart, I., Wang, X., Ng, E., Ren, C., and Brousse, O. (2016, January 10\u201314). Wudapt (World Urban Database and Access Portal Tools): An International Collaborative Project for Climate Relevant Physical Geography Data for the World\u2018s Cities. Proceedings of the 96th Amercian Meteorological Society Annual Meeting, New Orleans, LA, USA."},{"key":"ref_17","unstructured":"Feddema, J., Mills, G., and Ching, J. (2015, January 20\u201324). Demonstrating the Added Value of WUDAPT for Urban Climate Modelling. Proceedings of the ICUC9, Toulouse, France."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"199","DOI":"10.3390\/ijgi4010199","article-title":"Mapping local climate zones for a worldwide database of the form and function of cities","volume":"4","author":"Bechtel","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"18848","DOI":"10.1038\/s41598-019-55444-9","article-title":"Assessment of Local Climate Zone Classification Maps of Cities in China and Feasible Refinements","volume":"9","author":"Ren","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1109\/JSTARS.2012.2189873","article-title":"Classification of local climate zones based on multiple earth observation data","volume":"5","author":"Bechtel","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","unstructured":"G\u00e1l, T., Bechtel, B., and Unger, J. (2015, January 20\u201324). Comparison of two different local climate zone mapping methods. Proceedings of the ICUC9-9th International Conference on Urban Climates, Toulouse, France."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.isprsjprs.2020.04.008","article-title":"Local climate zone mapping as remote sensing scene classification using deep learning: A case study of metropolitan China","volume":"164","author":"Liu","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3397","DOI":"10.1109\/JSTARS.2017.2683484","article-title":"Classification of Local Climate Zones Using ASTER and Landsat Data for High-Density Cities","volume":"10","author":"Xu","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hu, J., Ghamisi, P., and Zhu, X.X. (2018). Feature Extraction and Selection of Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7090379"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1190","DOI":"10.1177\/1420326X18796545","article-title":"Local climate zone classification with different source data in Xi\u2019an, China","volume":"28","author":"He","year":"2019","journal-title":"Indoor Built Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Qiu, C., Schmitt, M., Mou, L., Ghamisi, P., and Zhu, X.X. (2018). Feature importance analysis for local climate zone classification using a residual convolutional neural network with multi-source datasets. Remote Sens., 10.","DOI":"10.3390\/rs10101572"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"014519","DOI":"10.1117\/1.JRS.15.014519","article-title":"Orbita hyperspectral satellite image for land cover classification using random forest classifier","volume":"15","author":"Mo","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Chen, C., Bagan, H., Xie, X., La, Y., and Yamagata, Y. (2021). Combination of sentinel-2 and palsar-2 for local climate zone classification: A case study of nanchang, China. Remote Sens., 13.","DOI":"10.3390\/rs13101902"},{"key":"ref_29","first-page":"1","article-title":"Evaluation of Luojia 1-01 nighttime light imagery for impervious surface detection: A comparison with NPP-VIIRS nighttime light data","volume":"81","author":"Ou","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.1177\/0042098009345540","article-title":"Residential redevelopment and the entrepreneurial local state: The implications of Beijing\u2019s shifting emphasis on urban redevelopment policies","volume":"46","author":"Shin","year":"2009","journal-title":"Urban Stud."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"235","DOI":"10.3828\/idpr.31.3.2","article-title":"Transportation implications of metropolitan spatial planning in mega-city Beijing","volume":"31","author":"Zhao","year":"2009","journal-title":"Int. Dev. Plan. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.isprsjprs.2021.03.019","article-title":"Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery","volume":"175","author":"Huang","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1080\/17538947.2017.1315462","article-title":"Development of S-NPP VIIRS global surface type classification map using support vector machines","volume":"11","author":"Zhang","year":"2018","journal-title":"Int. J. Digit. Earth"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.rse.2006.01.003","article-title":"Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction","volume":"101","author":"Jiang","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1177\/002076409904500102","article-title":"Application and Results of the Manchester Short Assessment of Quality of Life (Mansa)","volume":"45","author":"Priebe","year":"1999","journal-title":"Int. J. Soc. Psychiatry"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_39","unstructured":"Stewart, I.D., Oke, T.R., Bechtel, B., Foley, M.M., Mills, G., Ching, J., See, L., Alexander, P.J., O\u2019Connor, M., and Albuquerque, T. (2015, January 20\u201324). Generating WUDAPT\u2019s Specific Scale -dependent Urban Modeling and Activity Parameters: Collection of Level 1 and Level 2 Data. Proceedings of the ICUC9, Toulouse, France."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1078\/0176-1617-00887","article-title":"Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves","volume":"160","author":"Gitelson","year":"2003","journal-title":"J. Plant Physiol."},{"key":"ref_41","first-page":"158","article-title":"Wiegand and Richardson, \u2020 International Center for Agricultural Research in the Dry Areas 1990), natural vegetation (Friedl et al., 1994), and in (ICARDA)","volume":"71","author":"Thenkabail","year":"1995","journal-title":"Environ"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.csda.2006.12.030","article-title":"Unbiased split selection for classification trees based on the Gini Index","volume":"52","author":"Strobl","year":"2007","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"112105","DOI":"10.1016\/j.rse.2020.112105","article-title":"Improving land cover classification in an urbanized coastal area by random forests: The role of variable selection","volume":"251","author":"Zhang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1214\/ss\/1009213726","article-title":"Statistical modeling: The two cultures","volume":"16","author":"Breiman","year":"2001","journal-title":"Stat. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.net.2020.04.008","article-title":"ConvXGB: A new deep learning model for classification problems based on CNN and XGBoost","volume":"53","author":"Thongsuwan","year":"2021","journal-title":"Nucl. Eng. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1080\/15481603.2013.819161","article-title":"Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest","volume":"50","author":"Li","year":"2013","journal-title":"GIScience Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Park, S., Im, J., Park, S., Yoo, C., Han, H., and Rhee, J. (2018). Classification and mapping of paddy rice by combining Landsat and SAR time series data. Remote Sens., 10.","DOI":"10.3390\/rs10030447"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sim, S., Im, J., Park, S., Park, H., Ahn, M.H., and Chan, P.W. (2018). Icing detection over East Asia from geostationary satellite data using machine learning approaches. Remote Sens., 10.","DOI":"10.3390\/rs10040631"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Lee, J., Im, J., Kim, K., and Quackenbush, L.J. (2018). Machine learning approaches for estimating forest stand height using plot-based observations and Airborne LiDAR data. Forests, 9.","DOI":"10.3390\/f9050268"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1080\/15481603.2017.1302181","article-title":"A comparison of geographic datasets and field measurements to model soil carbon using random forests and stepwise regressions (British Columbia, Canada)","volume":"54","author":"Richardson","year":"2017","journal-title":"GIScience Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.isprsjprs.2018.01.018","article-title":"Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data","volume":"137","author":"Yoo","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"116599","DOI":"10.1016\/j.compstruct.2022.116599","article-title":"Improved arithmetic optimization algorithm and its application to carbon fiber reinforced polymer-steel bond strength estimation","volume":"306","author":"Shi","year":"2023","journal-title":"Compos. Struct."},{"key":"ref_54","first-page":"583","article-title":"Ensemble deep learning-based models to predict the resilient modulus of modified base materials subjected to wet-dry cycles","volume":"32","author":"BenemaranReza","year":"2023","journal-title":"Geomech. Eng."},{"key":"ref_55","unstructured":"Benemaran, R.S., Esmaeili-Falak, M., and Javadi, A. (2022). Predicting resilient modulus of flexible pavement foundation using extreme gradient boosting based optimised models. Int. J. Pavement Eng."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/S0034-4257(99)00083-8","article-title":"Integrating contextual information with per-pixel classification for improved land cover classification","volume":"71","author":"Stuckens","year":"2000","journal-title":"Remote Sens. Environ."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.1080\/01431160152558332","article-title":"A generalized confusion matrix for assessing area estimates from remotely sensed data","volume":"22","author":"Lewis","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_60","first-page":"79","article-title":"Object-based habitat mapping using very high spatial resolution multispectral and hyperspectral imagery with LiDAR data","volume":"59","author":"Onojeghuo","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1016\/j.ufug.2017.12.001","article-title":"Estimation of urban woody vegetation cover using multispectral imagery and LiDAR","volume":"29","author":"Ucar","year":"2018","journal-title":"Urban For. Urban Green."},{"key":"ref_62","first-page":"344","article-title":"Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on sentinel-2 and-3","volume":"23","author":"Clevers","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Sanlang, S., Cao, S., Du, M., Mo, Y., Chen, Q., and He, W. (2021). Integrating aerial lidar and very-high-resolution images for urban functional zone mapping. Remote Sens., 13.","DOI":"10.3390\/rs13132573"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zheng, B., and Hu, Y. (2020). Mapping local climate zones using arcGIS-based method and exploring land surface temperature characteristics in Chenzhou, China. Sustainability, 12.","DOI":"10.3390\/su12072974"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"111472","DOI":"10.1016\/j.rse.2019.111472","article-title":"Towards large-scale mapping of local climate zones using multitemporal Sentinel 2 data and convolutional neural networks","volume":"237","author":"Rosentreter","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.uclim.2017.10.001","article-title":"Mapping the local climate zones of urban areas by GIS-based and WUDAPT methods: A case study of Hong Kong","volume":"24","author":"Wang","year":"2018","journal-title":"Urban Clim."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3351\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:03:53Z","timestamp":1760126633000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3351"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,30]]},"references-count":66,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15133351"],"URL":"https:\/\/doi.org\/10.3390\/rs15133351","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,30]]}}}