Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Deep learning with uncertainty quantification for slum mapping using satellite imagery

Version 1 : Received: 6 August 2021 / Approved: 9 August 2021 / Online: 9 August 2021 (20:27:05 CEST)

How to cite: Fisher, T.; Gibson, H.; Salimi-Khorshidi, G.; Hassaine, A.; Cai, Y.; Rahimi, K.; Mamouei, M. Deep learning with uncertainty quantification for slum mapping using satellite imagery. Preprints 2021, 2021080209 (doi: 10.20944/preprints202108.0209.v1). Fisher, T.; Gibson, H.; Salimi-Khorshidi, G.; Hassaine, A.; Cai, Y.; Rahimi, K.; Mamouei, M. Deep learning with uncertainty quantification for slum mapping using satellite imagery. Preprints 2021, 2021080209 (doi: 10.20944/preprints202108.0209.v1).

Abstract

Over a billion people live in slums, with poor sanitation, education, property rights and working conditions having direct impact on current residents and future generations. A key problem in relation to slums is slum mapping. Without delineations of where all slum settlements are, informed decisions cannot be made by policymakers in order to benefit the most in need. Satellite images have been used in combination with machine learning models to try and fill the gap in data availability of slum locations. Deep learning has been used on RGB images with some success but since labeled satellite images of slums are relatively low quality and the physical/visual manifestation of slums significantly varies within and across countries, it is important to quantify the uncertainty of predictions for reliable application in downstream tasks. Our solution is to train Monte Carlo dropout U-Net models on multispectral 13-band Sentinel-2 images from which we can calculate pixelwise epistemic (model) and aleatoric (data) uncertainty in our predictions. We trained our model on labelled images of Mumbai and verified our epistemic and aleatoric uncertainty quantification approach using altered models trained on modified datasets. We also used SHAP values to investigate how the different features contribute towards the model’s predictions and this showed that certain short-wave infrared and red-edge image bands are powerful features for determining the locations of slums within images. Having created our model with uncertainty quantification, in the future it can be applied to downstream tasks and decision-makers will know where predictions have been made with low uncertainty, giving them greater confidence in its deployment.

Keywords

slums; informal settlements; deep learning; machine learning; uncertainty quantification

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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