Mapping Urban Land Use in India and Mexico using Remote Sensing and Machine Learningby , , , and -
This technical note describes the data sources and methodology underpinning a computer system for the automated generation of land use/land cover (LULC) maps of urban areas based on medium-resolution (10–30m/pixel) satellite imagery. The system and maps deploy the LULC taxonomy of the Atlas of Urban Expansion—2016 Edition: open, nonresidential, roads, and four types of residential space. We used supervised machine learning techniques to apply this taxonomy at scale. Distinguishing between recognizable, clearly defined types of land use within a built-up area, rather than merely delineating artificial land cover, enables a huge variety of potential applications for policy, planning, and research. We demonstrate the training and application of machine-learning-based algorithms to characterize LULC over a large spatial and temporal range in a way that avoids many of the onerous constraints and expenses of the traditional LULC mapping process: manual identification and classification of features.
This document supersedes the previous technical note, Spatial Characterization of Urban Land Use through Machine Learning, and the methodology described here supersedes our previously reported techniques.
Full executive summary available in the paper.
- Dataset: areal (6-category) LULC of urban India (2016 & 2019) on Resource Watch
- Dataset: areal (6-category) LULC of selected cities in Mexico (2018) on Resource Watch
- Dataset: roads (binary) LULC of selected cities in Mexico (2018) on Resource Watch
- Map: combined areal (6-category) and roads LULC of selected cities in Mexico (2018) on Resource Watch
- Github: comprehensive workflow for characterizing urban land use at scale with machine learning