Deep residential representations: Using unsupervised learning to unlock elevation data for geo-demographic prediction

Abstract

LiDAR (short for “Light Detection And Ranging” or “Laser Imaging, Detection, And Ranging”) technology can be used to provide detailed three-dimensional elevation maps of urban and rural landscapes. The geographically granular and open-source nature of this data lends itself to an array of societal, organisational and business applications where geo-demographic type data is utilised. However, the complexity involved in processing this multi-dimensional data in raw form has thus far restricted its practical adoption. This paper proposes a series of convenient task-agnostic tile elevation embeddings to address this challenge, using recent advances from unsupervised Deep Learning. We test the potential of our embeddings by predicting seven English indices of deprivation (2019) for small geographies in the Greater London area. These indices cover a range of socio-economic outcomes and serve as a proxy for a wide variety of potential downstream tasks to which the embeddings can be applied. We consider the suitability of this data not just on its own but also as an auxiliary source of data in combination with demographic features, thus providing a realistic use case for the embeddings. Having trialled various model/embedding configurations, we find that our best performing embeddings lead to Root-Mean-Squared-Error (RMSE) improvements of up to 21% over using standard demographic features alone. We also demonstrate how our embedding pipeline, using Deep Learning combined with K-means clustering, produces coherent tile segments which allow the latent embedding features to be interpreted.