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

arXiv: https://arxiv.org/abs/2112.01421 · HTML: https://mlsanity.com/paper/2112.01421/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 89 references that are in the map.
- First posted on arXiv 2 Dec 2021.

## Builds on (most cited first, 12 of 12)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Take a Look Around: Using Street View and Satellite Images to Estimate House Prices](https://mlsanity.com/paper/1807.07155/) (2018)
- [Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees](https://mlsanity.com/paper/1802.08872/) (2018)
- [Learning and Recognizing Archeological Features from LiDAR Data](https://mlsanity.com/paper/2004.02099/) (2020)

## Built on by (most cited first, 2 of 2)

- [Self-supervised Learning in Remote Sensing: A Review](https://mlsanity.com/paper/2206.13188/) (2022)
- [Revealing Geography-Driven Signals in Zone-Level Claim Frequency Models: An Empirical Study using Environmental and Visual Predictors](https://mlsanity.com/paper/2604.21893/) (2026)

## Abstract (arXiv)

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.