  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. To date, airborne LiDAR imaging
has been predominantly confined to the environmental and archaeological
domains. However, the geographically granular and open-source nature of this
data also lends itself to an array of societal, organizational and business
applications where geo-demographic type data is utilised. Arguably, the
complexity involved in processing this multi-dimensional data has thus far
restricted its broader adoption. In this paper, we propose 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 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.
