期刊
REMOTE SENSING OF ENVIRONMENT
卷 205, 期 -, 页码 253-275出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.rse.2017.11.026
关键词
Urbanization; Built-up land cover; Nighttime light; Image classification; Google Earth Engine
资金
- National Science Foundation [EAR-1204774, DMS-1419593]
- U.S. Department of Agriculture NIFA [2015-67003-23508]
- National Science Foundation Sustainability Research Network (SRN) [1444758]
- Urban Water Innovation Network (UWIN)
- Arizona State University
- International Growth Centre (IGC) [89448]
- Center on Global Transformation at UCSD
- Division Of Earth Sciences [1204774] Funding Source: National Science Foundation
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.
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