Towards Geodemographics from Satellite Images

Keywords: Geodemographics, Satellite Images, Machine Learning
Abstract Type: Paper Abstract

Abstract

The increased availability of high-resolution multispectral imagery captured by remote sensing platforms provides new opportunities for the characterization and differentiation of urban context. The discovery of generalized latent representations from such data are however under researched within the social sciences. 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 summarized 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 utilized within the study and the interaction between the geography of the input data and the learned structure. The future directions for the work are considered with reference to linkage within more traditional and comprehensive geodemographic classification frameworks.