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. Preprints2021, 2021080209. https://doi.org/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. https://doi.org/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. Preprints2021, 2021080209. https://doi.org/10.20944/preprints202108.0209.v1
APA Style
Fisher, T., Gibson, H., Salimi-Khorshidi, G., Hassaine, A., Cai, Y., Rahimi, K., & Mamouei, M. (2021). Deep learning with uncertainty quantification for slum mapping using satellite imagery. Preprints. https://doi.org/10.20944/preprints202108.0209.v1
Chicago/Turabian Style
Fisher, T., Kazem Rahimi and Mohammad Mamouei. 2021 "Deep learning with uncertainty quantification for slum mapping using satellite imagery" Preprints. https://doi.org/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
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
