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.
Go to Resource| Field | Value |
|---|---|
| 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) |
| Stable Link | http://patung.lancis.ecologia.unam.mx/tellman/tellman/Urbanizacion/landsat/Goldblatt%20et%20al.%20-%202018%20-%20Using%20Landsat%20and%20nighttime%20lights%20for%20supervised%20.pdf |
| 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 |