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Goldblatt, Ran; Stuhlmacher, Michelle F.; Tellman, Beth; Clinton, Nicholas; Hanson, Gordon; Georgescu, Matei; Wang, Chuyuan; Serrano-Candela, Fidel; Khandelwal, Amit K.; Cheng, Wan-Hwa; Balling, Robert C.
Remote sensing of environment, February 2018, 2018-02-00, 20180201, Volume: 205Journal Article
Reliable representations of global urban extent remain limited, hindering scientific progress across a range of disciplines that study functionality of sustainable cities. We present an efficient and low-cost machine-learning approach for pixel-based image classification of built-up areas at a large geographic scale using Landsat data. Our methodology combines nighttime-lights data and Landsat 8 and overcomes the lack of extensive ground-reference data. We demonstrate the effectiveness of our methodology, which is implemented in Google Earth Engine, through the development of accurate 30m resolution maps that characterize built-up land cover in three geographically diverse countries: India, Mexico, and the US. Our approach highlights the usefulness of data fusion techniques for studying the built environment and is a first step towards the creation of an accurate global-scale map of urban land cover over time. •An approach is proposed to map built-up land cover at a large geographical scale.•Our data fusion approach utilizes nighttime-lights data and Landsat imagery.•The approach overcomes the lack of extensive ground-reference data for urban research.•Hexagonal tessellation partition improves classification of heterogeneous land cover.•High quality maps of built-up LC are produced for 3 geographically diverse countries.
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