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Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover

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 groundreference data. We demonstrate the effectiveness of our methodology, which is implemented in Google Earth Engine, through the development of accurate 30 m 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.

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Author(s) Ran Goldblatt, Michelle F. Stuhlmacher, Beth Tellman, Nicholas Clinton, Gordon Hanson, Matei Georgescu, Chuyuan Wang, Fidel Serrano-Candela, Amit K. Khandelwal , Wan-Hwa Cheng, Robert C. Balling Jr
Last Updated February 11, 2021, 19:27 (UTC)
Created December 8, 2020, 00:20 (UTC)
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Date 2018-02-01
Publishing Body Remote Sensing of Environment
Content Type Publications
Primary Category Land Use & Land Cover
Sub Category LULC
Country Name Global
Publishing Organization New Light Technologies