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

Abstract

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.

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“…Jochem and Tatem (2021), for example, published an R package that uses building footprints to classify settlement types, though it has only been tested in Europe and imperfectly distinguishes between urban settlements types [58]. The World Resources Institute released Python code to distinguish urban land use types, including "residential informal" land use, from Sentinel-II imagery and demonstrated its application in India and Mexico, though substantial bespoke training data were required [59,60]. If, and when, a routine accurate map of deprived urban areas becomes available across cities, population distribution modellers might consider dividing a country by settlement type before modelling (e.g.…”

Improving the Accuracy of Gridded Population Estimates in Cities and Slums to Monitor SDG 11: Evidence from a Simulation Study in Namibia

“…Jochem and Tatem (2021), for example, published an R package that uses building footprints to classify settlement types, though it has only been tested in Europe and imperfectly distinguishes between urban settlements types [55]. The World Resources Institute released Python code to distinguish urban land use types, including "residential informal" land use, from Sentinel-II imagery and demonstrated its application in India and Mexico, though substantial bespoke training data were required [56,57]. If, and when, a routine accurate map of deprived urban areas becomes available across cities, population distribution modellers might consider dividing a country by settlement type before modelling (e.g.…”