Phenotyping urban built and natural environments with high-resolution satellite images and unsupervised deep learning.

ABSTRACT: Cities in the developing world are expanding rapidly, and undergoing changes to their roads, buildings, vegetation, and other land use characteristics. Timely data are needed to ensure that urban change enhances health, wellbeing and sustainability. We present and evaluate a novel unsupervised deep clustering method to classify and characterise the complex and multidimensional built and natural environments of cities into interpretable clusters using high-resolution satellite images. We applied our approach to a high-resolution (0.3 m/pixel) satellite image of Accra, Ghana, one of the fastest growing cities in sub-Saharan Africa, and contextualised the results with demographic and environmental data that were not used for clustering. We show that clusters obtained solely from images captur

SUBMITTER: Metzler AB 

PROVIDER: S-EPMC7615085 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature