Mapping Urban India: Comprehensive Land Use/Land Cover Classification of Urban Areas Using Public Imagery and Machine Learning

An ever-larger share of humanity lives in urban areas, a trend expected to continue in the coming decades. Whether considering the economic, demographic, environmental, or societal dimensions of human activity and impact, the ways in which cities change in order to accommodate swelling urban populations—or fail to do so—will have outsize significance to human well-being as well as local and global environmental outcomes. Credible land use / land cover (LULC) maps are a vital tool for monitoring and measuring these changes, but the capacity to produce this information is often limited, especially in some of the urban areas expected to undergo the most disruption and growth. Automated mapping, driven by satellite imagery and machine learning, may offer a solution.
We present a map classifying LULC across all of urban India at 5-meter resolution. Using only publicly available inputs—satellite imagery from the Sentinel-2 constellation and manually coded ground-truth data from the Atlas of Urban Expansion—we constructed training samples representing fourteen Indian cities for supervised machine learning. By feeding these samples into a convolutional neural network, we trained a single model capable of classifying LULC across a range of environments and urban morphologies. The trained model was then applied to satellite imagery to make comprehensive predictions for all urban areas in India, as identified by the Global Human Settlement Layer. We quantified model performance by comparing predictions to reserved, validation ground-truth from the Atlas of Urban Expansion, where available. All data processing and storage as well as model creation, training, and application was executed in the commercial cloud within a highly scalable architecture. This permits continual map generation as new imagery is collected, allowing for low-cost monitoring of urban change, in near real-time and across entire countries. The methods, architecture, and training data can be quickly and straightforwardly transferred to alternative geographies and imagery sources, and can be applied to different time periods, allowing historical change detection.