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Poverty rate prediction using multi-modal survey and earth observation data
This work presents an approach for combining household demographic and living standards survey questions with features derived from satellite imagery to predict the poverty rate of a region.
Proceedings of the NeurIPS 2021 Workshop on Machine Learning for the Developing World: Global Challenges
These are the proceedings of the 5th workshop on Machine Learning for the Developing World (ML4D), held as part of the Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS) on December 14th, 2021.
A Higher Purpose: Measuring Electricity Access Using High-Resolution Daytime Satellite Imagery
Our regressions show $R^2$ scores of 78% and 80% in estimating the number of electrified buildings and number of residential electrified building in images respectively.
Predicting Levels of Household Electricity Consumption in Low-Access Settings
This is the first study of it's kind in low-income settings that attempts to predict a building's consumption and not that of an aggregate administrative area.
Learning to segment from misaligned and partial labels
Open source infrastructure annotations like OpenStreetMaps (OSM) are representative of this issue: while OSM labels provide global insights to road and building footprints, noisy and partial annotations limit the performance of segmentation algorithms that learn from them.
