Publications

Goldblatt, R; Stuhlmacher, MF; Tellman, B; Clinton, N; Hanson, G; Georgescu, M; Wang, CY; Serrano-Candela, F; Khandelwal, AK; Cheng, WH; Balling, RC (2018). Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. REMOTE SENSING OF ENVIRONMENT, 205, 253-275.

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.

DOI:
10.1016/j.rse.2017.11.026

ISSN:
0034-4257