Klemmer, Konstantin, Yeboah, Godwin, Porto de Albuquerque, João and Jarvis, Stephen A. (2020) Population mapping in informal settlements with high-resolution satellite imagery and equitable ground-truth. In: ML-IRL Workshop, International Conference on Learning Representations 2020, Addis Ababa, Ethiopia, 26 Apr 2020

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Abstract

We propose a generalizable framework for the population estimation of dense, informal settlements in low-income urban areas–so called ’slums’–using high-resolution satellite imagery. Precise population estimates are a crucial factor for efficient resource allocations by government authorities and NGO’s, for instance in medical emergencies. We utilize equitable ground-truth data, which is gathered in collaboration with local communities: Through training and community mapping, the local population contributes their unique domain knowledge, while also maintaining agency over their data. This practice allows us to avoid carrying forward potential biases into the modeling pipeline, which might arise from a less
rigorous ground-truthing approach. We contextualize our approach in respect to the ongoing discussion within the machine learning community, aiming to make real-world machine learning applications more inclusive, fair and accountable. Because of the resource intensive ground-truth generation process, our training data is limited. We propose a gridded population estimation model, enabling flexible and customizable spatial resolutions. We test our pipeline on three experimental site in Nigeria, utilizing pre-trained and fine-tune vision networks to overcome data sparsity. Our findings highlight the difficulties of transferring common benchmark models to real-world tasks. We discuss this and propose steps forward.

Item Type:	Conference Item (UNSPECIFIED)
Subjects:	H Social Sciences > HB Economic Theory H Social Sciences > HV Social pathology. Social and public welfare Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QH Natural history
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH):	Population -- Nigeria, Population forecasting , Population -- Mathematical models , Population forecasting -- Simulation methods, Population policy, Population viability analysis , Remote-sensing images, Slums -- Nigeria
Official Date:	2020
Dates:	Date Event 2020 Available 24 February 2020 Accepted
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	14 April 2020
Date of first compliant Open Access:	14 April 2020
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID EP/L016400/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266 EP/N510129/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266 EP/N510129/1 Alan Turing Institute http://dx.doi.org/10.13039/100012338
Title of Event:	ML-IRL Workshop, International Conference on Learning Representations 2020
Type of Event:	Workshop
Location of Event:	Addis Ababa, Ethiopia
Date(s) of Event:	26 Apr 2020
Related URLs:	Organisation
Open Access Version:	Publisher

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