A perspective on the interpretability of poverty maps derived from Earth Observation
Watmough, Gary R. (University of Edinburgh)
Brockington, Dan (Universitat Autònoma de Barcelona. Departament de Dret Privat)
Marcinko, Charlotte L.J. (Government Actuary's Department)
Hall, Ola (Lund University)
Pritchard, Rose (University of Manchester)
Berchoux, Tristan (University of Montpellier)
Gibson, Lesley (Stellenbosch University)
Delamonica, Enrique (United Nations International Children's Emergency Fund)
Boyd, Doreen (University of Nottingham)
Mlambo, Reason (University of Edinburgh)
Ó Héir, Seán (University of Edinburgh)
Seth, Sohan (University of Edinburg)
Universitat Autònoma de Barcelona. Institut de Ciència i Tecnologia Ambientals

Data:	2025
Resum:	The use of Earth Observation Data and Machine Learning models to generate gridded micro-level poverty maps has increased in recent years, with several high-profile publications. producing some compelling results. Poverty alleviation remains one of the most critical global challenges. Earth Observation (EO) technologies represent a promising avenue to enhance our ability to address poverty through improved data availability. However, global poverty maps generated by these technologies tend to oversimplify the complex and nuanced nature of poverty preventing progression from proof-of-concept studies to technology that can be deployed in decision making. We provide a perspective on the EO4Poverty field with a focus on areas that need attention. To increase the awareness of what is possible with this technology and reduce the discomfort with model-based estimates, we argue that the EO4Poverty models could and should focus on explainability and operationalizability alongside accuracy and robustness. The use of raw imagery in black-box models results in predictions that appear highly accurate but that are often flawed when investigated in specific local contexts. These models will benefit for incorporating interpretable geospatial features that are directly linked to local context. The use of domain expertise from local end users could make model predictions accessible and more transferable to hard-to-reach areas with little training data.
Nota:	Unidad de excelencia María de Maeztu CEX2019-000940-M
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Publicat a:	Science of Remote Sensing, Vol. 12 (December 2025) , art. 100298, ISSN 2666-0172

DOI: 10.1016/j.srs.2025.100298

8 p, 3.6 MB

El registre apareix a les col·leccions:
Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències > Institut de Ciència i Tecnologia Ambientals (ICTA)
Articles > Articles de recerca
Articles > Articles publicats