Estimating generalized measures of local neighbourhood context from multispectral satellite images using a convolutional neural network

• Published in: Computers, environment and urban systems environment and urban systems, 2022-07, Vol.95, p.101802, Article 101802
• Main Authors: Singleton, Alex, Arribas-Bel, Dani, Murray, John, Fleischmann, Martin
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Cluster analysis; Context; Convolutional neural networks; Deep learning; High resolution; Image resolution; Machine learning; Multispectral satellite imagery; Remote sensing; Satellite imagery; Urban morphology
• ISSN: 0198-9715; 1873-7587
• DOI: 10.1016/j.compenvurbsys.2022.101802

The increased availability of high-resolution multispectral imagery captured by remote sensing platforms provides new opportunities for the characterisation and differentiation of urban context. The discovery of generalized latent representations from such data are however under researched within the social sciences. As such, this paper exploits advances in machine learning to implement a new method of capturing measures of urban context from multispectral satellite imagery at a very small area level through the application of a convolutional autoencoder (CAE). The utility of outputs from the CAE is enhanced through the application of spatial weighting, and the smoothed outputs are then summarised using cluster analysis to generate a typology comprising seven groups describing salient patterns of differentiated urban context. The limits of the technique are discussed with reference to the resolution of the satellite data utilised within the study and the interaction between the geography of the input data and the learned structure. The method is implemented within the context of Great Britain, however, is applicable to any location where similar high resolution multispectral imagery are available. •We demonstrate the utility of a Convolutional Neural Network in the characterisation of urban context from satellite imagery.•Context is described effectively using an unsupervised convolutional autoencoder applied to Sentinel 2 multispectral imagery.•Spatial weighting enhances the quality of a multidimensional classification of local neighbourhood context.•A seven-group typology of context is presented for every postcode in Great Britain.