Satellite imagery of a densely built settlement

IEEE Research Paper · Co-Author

Harnessing Satellite Imagery with Convolutional Neural Networks for Poverty Prediction

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA) · December 2024

PythonPyTorchCNNComputer Vision

Ground-truth poverty data is expensive and slow to collect — it usually means door-to-door household surveys across regions that are often hard to reach. This paper asks a different question: can a computer look at a satellite photograph and estimate how poor an area is, without anyone ever setting foot there?

The approach uses a convolutional neural network (CNN) — the same class of model that powers modern image recognition — trained on satellite imagery of Africa. Rather than being told explicitly what to look for, the network learns on its own which visual patterns correlate with poverty: road density, the layout and materials of buildings, vegetation cover, and general land use.

Satellite imagery of settlement adjacent to green space
Dense settlement bordering open land — the kind of texture and layout the model learns from.
Satellite imagery of settlement with unpaved roads
Roof material, road paving, and building density all carry signal about an area's economic conditions.

To make sure the model was actually learning something generalizable, the dataset was split 70% for training and 30% for testing. The model never saw the test images during training, so its performance on that held-out set is a fair measure of how well it would work on a brand-new region.

Methodology flowchart: image dataset to data visualization, preprocessing, conversion, splitting, training, and result
Fig. 1 — the full pipeline: raw image dataset → preprocessing → CNN training → evaluation on held-out test data.

The final model reached 90% accuracy on the test set — a strong result for a problem this indirect, where the model has to infer an economic condition purely from visual texture and geometry.

Satellite imagery of a rural village settlement
A more rural settlement pattern from the same imagery set — sparser buildings, different road structure.

The paper was co-authored, presented at the IEEE ICAIQSA conference, and published to IEEE Xplore.