A Two-Stage Approach to Improve Poverty Mapping Spatial Resolution
Name
remotesensing-18-00427.pdf
Size
2.07 MB
Format
Adobe PDF
Checksum (MD5)
4cf61a72abff46d8c898ed0f053ffce0
Author(s)
Salas, Joaquín
Zea-Ortiz, Marivel
Vera, Pablo
Wood, Danielle
Date Issued
January 28, 2026
Journal
Remote Sensing
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Salas, J.; Zea-Ortiz, M.; Vera, P.; Wood, D. A Two-Stage Approach to Improve Poverty Mapping Spatial Resolution. Remote Sens. 2026, 18, 427.
Version
Final published version
Abstract
Global extreme poverty has fallen dramatically over the past two centuries, yet hundreds of millions remain impoverished, underscoring the need for scalable monitoring tools. In Mexico, poverty metrics are available only sporadically in terms of time and space (e.g., every 5 years at the municipal level), making it difficult for decision-makers to access reliable, up-to-date, and sufficiently detailed information, highlighting the need for higher-resolution, timely methods. To address this problem, we propose a two-stage approach that combines socioeconomic and Earth Observations-based data. Initially, a machine learning model maps census variables to official poverty indicators belonging to a multidimensional model, yielding fine-scale poverty estimates. A census-based model trained with eXtreme Gradient Boosting (XGBoost) achieved a determination coefficient (𝑅2) of approximately 0.842, indicating strong agreement with official poverty figures and providing high-resolution proxies. Afterward, we use features based on remote observations to predict these poverty estimates at a 469 m grid scale. In this case, advanced foundation models outperformed other machine learning (ML) approaches, achieving an 𝑅2 of 0.683. While foundation models enable more accurate, fine-scale poverty mapping and could accelerate poverty assessments, their use comes at a heavy price in terms of carbon emissions.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
http://dx.doi.org/10.3390/rs18030427
