<reponame>akhandelwal8/akhandelwal.github.io<gh_stars>0
@article{GSTCHGWSCKCB2018rse,
title = "Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover",
journal = "Remote Sensing of Environment",
volume = "205",
pages = "253 - 275",
year = "2018",
issn = "0034-4257",
doi = "https://doi.org/10.1016/j.rse.2017.11.026",
url = "http://www.sciencedirect.com/science/article/pii/S0034425717305758",
author = "<NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME>",
keywords = "Urbanization, Built-up land cover, Nighttime light, Image classification, Google Earth Engine",
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 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."
}