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Previous slum mapping studies using high-resolution or very-high-resolution (HR\/VHR) remotely sensed (RS) images are commonly not suitable for city-wide scale tasks. This study aims to efficiently generate a slum map on a city-wide scale using freely accessed multispectral medium-resolution (MR) Sentinel-2 images. Composite slum spectral indices (CSSIs) were initially proposed based on the shapes of spectral profiles of slums and nonslums and directly represent slum characteristics. Specifically, CSSI-1 denotes the normalized difference between the shortwave infrared bands and the red edge band, while CSSI-2 denotes the normalized difference between the blue band and the green band. Furthermore, two methods were developed to test the effectiveness of CSSIs on slum mapping, i.e., the threshold-based method and the machine learning (ML)-based method. Experimental results show that the threshold-based method and the ML-based method achieve intersection over unions (IoU) of 43.89% and 54.45% in Mumbai, respectively. The accuracies of our methods are comparable to or even higher than the accuracies reported by existing methods using HR\/VHR images and transfer learning. The threshold-based method exhibits a promising performance in mapping slums larger than 5 ha, while the ML-based method refines mapping accuracies for slum pockets smaller than 5 ha. The threshold-based method and the ML-based method produced the slum map in Mumbai in 2 and 28 min, respectively. Our methods are suitable for rapid large-area slum mapping owing to the high data availability of Sentinel-2 images and high computational efficiency.<\/jats:p>","DOI":"10.3390\/rs15194671","type":"journal-article","created":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T10:46:21Z","timestamp":1695552381000},"page":"4671","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Mapping Slums in Mumbai, India, Using Sentinel-2 Imagery: Evaluating Composite Slum Spectral Indices (CSSIs)"],"prefix":"10.3390","volume":"15","author":[{"given":"Feifei","family":"Peng","sequence":"first","affiliation":[{"name":"Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China"},{"name":"College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"}]},{"given":"Wei","family":"Lu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6219-6251","authenticated-orcid":false,"given":"Yunfeng","family":"Hu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4994-7644","authenticated-orcid":false,"given":"Liangcun","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,23]]},"reference":[{"key":"ref_1","unstructured":"Secretary-General, U.N. 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