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• W4317181824 abstract "BACKGROUND AND AIM: Cities in the developing world are expanding rapidly and undergoing changes to their roads, housing and other buildings, vegetation, and land use characteristics. Timely data are needed to ensure that urban change enhance health, wellbeing and sustainability. METHODS: We characterise, as mutually exclusive clusters, the complex, multidimensional, built and natural environments in cities with high-resolution satellite images and unsupervised deep clustering. We apply our approach to Accra, Ghana, one of the fastest growing cities in the developing world, and contextualise the resultant clusters with demographic and environmental data that were not used for clustering. RESULTS: We show that image-based clusters captured distinct features of the urban built environment (building count, size, density, and orientation; length and arrangement of roads), vegetation, water, and population, either as a unique defining characteristic (e.g., bodies of water or dense vegetation) or in combination (e.g., buildings surrounded by vegetation or sparsely populated areas intermixed with roads). Clusters that were based on single defining characteristics were robust to the spatial scale of analysis and choice of cluster number, whereas those based on a combination of defining characteristics changed based on scale and number of clusters. CONCLUSION: The results demonstrate that satellite data and unsupervised deep learning provide a cost-effective interpretable and scalable approach for real-time tracking of sustainable urban development, especially where traditional environmental and demographic data are limited and not frequently updated. Our approach has multiple urban environmental applications, such as providing ground data for tracking and measuring urban health, air- and noise pollution, urban connectivity and road traffic, as well as city growth in cities across Africa and beyond. KEYWORDS: Big data, satellite imagery, deep learning, built environment; urban growth, unsupervised machine learning" @default.
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• W4317181824 date "2022-09-18" @default.
• W4317181824 modified "2023-10-18" @default.
• W4317181824 title "Characterization of urban built and natural environments with high-resolution satellite images and unsupervised deep learning" @default.
• W4317181824 doi "https://doi.org/10.1289/isee.2022.o-op-019" @default.
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