@article{ba507792bbd04d4fbce8df19a87d6aae,
title = "Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover",
abstract = "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 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.",
keywords = "Built-up land cover, Google Earth Engine, Image classification, Nighttime light, Urbanization",
author = "Ran Goldblatt and Stuhlmacher, {Michelle F.} and Beth Tellman and Nicholas Clinton and Gordon Hanson and Matei Georgescu and Chuyuan Wang and Fidel Serrano-Candela and Khandelwal, {Amit K.} and Cheng, {Wan Hwa} and Robert Balling",
note = "Funding Information: MG was supported by the National Science Foundation Grants EAR-1204774 and DMS-1419593, the U.S. Department of Agriculture NIFA grant 2015-67003-23508. MG and MS were also supported by the National Science Foundation Sustainability Research Network (SRN) Cooperative Agreement 1444758, the Urban Water Innovation Network (UWIN). MS was also supported by Dr. B. L. Turner II's Gilbert F. White Environment and Society Fellowship at Arizona State University. AK, RG and GH were supported by funds from the International Growth Centre (IGC project number 89448). RG and GH were supported by the Center on Global Transformation at UCSD. We thank the Institute of Geography, National Autonomous University of Mexico, Dr. Armando Peralta Higuera, who provided support for two students, Juan Alberto Guerra Moreno and Kimberly Mendez Gomez for validating the algorithm in Mexico. Funding Information: MG was supported by the National Science Foundation Grants EAR-1204774 and DMS-1419593 , the U.S. Department of Agriculture NIFA grant 2015-67003-23508 . MG and MS were also supported by the National Science Foundation Sustainability Research Network (SRN) Cooperative Agreement 1444758 , the Urban Water Innovation Network (UWIN). MS was also supported by Dr. B. L. Turner II's Gilbert F. White Environment and Society Fellowship at Arizona State University. AK, RG and GH were supported by funds from the International Growth Centre (IGC project number 89448 ). RG and GH were supported by the Center on Global Transformation at UCSD . We thank the Institute of Geography, National Autonomous University of Mexico, Dr. Armando Peralta Higuera, who provided support for two students, Juan Alberto Guerra Moreno and Kimberly Mendez Gomez for validating the algorithm in Mexico. Publisher Copyright: {\textcopyright} 2017 Elsevier Inc.",
year = "2018",
month = feb,
doi = "10.1016/j.rse.2017.11.026",
language = "English (US)",
volume = "205",
pages = "253--275",
journal = "Remote Sensing of Environment",
issn = "0034-4257",
publisher = "Elsevier Inc.",
}