Title: Poverty Mapping Using Convolutional Neural Networks Trained on High and Medium Resolution Satellite Images, With an Application in Mexico,
Abstract: Mapping the spatial distribution of poverty in developing countries remains
an important and costly challenge. These "poverty maps" are key inputs for
poverty targeting, public goods provision, political accountability, and impact
evaluation, that are all the more important given the geographic dispersion of
the remaining bottom billion severely poor individuals. In this paper we train
Convolutional Neural Networks (CNNs) to estimate poverty directly from high and
medium resolution satellite images. We use both Planet and Digital Globe
imagery with spatial resolutions of 3-5 sq. m. and 50 sq. cm. respectively,
covering all 2 million sq. km. of Mexico. Benchmark poverty estimates come from
the 2014 MCS-ENIGH combined with the 2015 Intercensus and are used to estimate
poverty rates for 2,456 Mexican municipalities. CNNs are trained using the 896
municipalities in the 2014 MCS-ENIGH. We experiment with several architectures
(GoogleNet, VGG) and use GoogleNet as a final architecture where weights are
fine-tuned from ImageNet. We find that 1) the best models, which incorporate
satellite-estimated land use as a predictor, explain approximately 57% of the
variation in poverty in a validation sample of 10 percent of MCS-ENIGH
municipalities; 2) Across all MCS-ENIGH municipalities explanatory power
reduces to 44% in a CNN prediction and landcover model; 3) Predicted poverty
from the CNN predictions alone explains 47% of the variation in poverty in the
validation sample, and 37% over all MCS-ENIGH municipalities; 4) In urban areas
we see slight improvements from using Digital Globe versus Planet imagery,
which explain 61% and 54% of poverty variation respectively. We conclude that
CNNs can be trained end-to-end on satellite imagery to estimate poverty,
although there is much work to be done to understand how the training process
influences out of sample validation.