Time Series of Satellite Imagery Improve Deep Learning Estimates of Neighborhood-Level Poverty in Africa
Paper i proceeding, 2023

To combat poor health and living conditions, policymakers in Africa require temporally and geographically granular data measuring economic well-being. Machine learning (ML) offers a promising alternative to expensive and time-consuming survey measurements by training models to predict economic conditions from freely available satellite imagery. However, previous efforts have failed to utilize the temporal information available in earth observation (EO) data, which may capture developments important to standards of living. In this work, we develop an EO-ML method for inferring neighborhood-level material-asset wealth using multi-temporal imagery and recurrent convolutional neural networks.1 Our model outperforms state-of-the-art models in several aspects of generalization, explaining 72% of the variance in wealth across held-out countries and 75% held-out time spans. Using our geographically and temporally aware models, we created spatiotemporal material-asset data maps covering the entire continent of Africa from 1990 to 2019, making our data product the largest dataset of its kind. We showcase these results by analyzing which neighborhoods are likely to escape poverty by the year 2030, which is the deadline for when the Sustainable Development Goals (SDG) are evaluated.

Författare

Markus Pettersson

Chalmers, Data- och informationsteknik, Data Science och AI

Linköpings universitet

Mohammad Kakooei

Linköpings universitet

Chalmers, Data- och informationsteknik, Data Science och AI

Julia Ortheden

Student vid Chalmers

Fredrik Johansson

Chalmers, Data- och informationsteknik, Data Science och AI

Adel Daoud

Göteborgs universitet

Stanford University

Linköpings universitet

IJCAI International Joint Conference on Artificial Intelligence

10450823 (ISSN)

Vol. 2023-August 6165-6173
9781956792034 (ISBN)

32nd International Joint Conference on Artificial Intelligence, IJCAI 2023
Macao, China,

Ämneskategorier

Nationalekonomi

Sannolikhetsteori och statistik

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Senast uppdaterat

2024-08-08