Mapping Urban Land Use in India and Mexico using Remote Sensing and Machine Learning

Peter Kerins, Brook Guzder-Williams, Eric Mackres, Taufiq Rashid, Eric Pietraszkiewicz
2021 WRI Publications  
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 (10-30 m/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
more » ... nizable, 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.
doi:10.46830/writn.20.00048 fatcat:e7uksq2lg5dnjnuse23xvc66di