Large-scale School Mapping using Weakly Supervised Deep Learning for Universal School Connectivity

TL;DR

This paper presents a scalable, weakly supervised deep learning approach combining vision transformers and CNNs to accurately locate schools in satellite images, aiding global connectivity efforts especially in low-resource settings.

Contribution

It introduces a novel weakly supervised deep learning method that achieves high accuracy in school detection using minimal annotations, enabling large-scale mapping in African countries.

Findings

Achieved AUPRC above 0.96 in 10 African countries.

Generated nationwide school location maps for African countries.

Developed an interactive web tool for validation and planning.

Abstract

Improving global school connectivity is critical for ensuring inclusive and equitable quality education. To reliably estimate the cost of connecting schools, governments and connectivity providers require complete and accurate school location data - a resource that is often scarce in many low- and middle-income countries. To address this challenge, we propose a cost-effective, scalable approach to locating schools in high-resolution satellite images using weakly supervised deep learning techniques. Our best models, which combine vision transformers and convolutional neural networks, achieve AUPRC values above 0.96 across 10 pilot African countries. Leveraging explainable AI techniques, our approach can approximate the precise geographical coordinates of the school locations using only low-cost, classification-level annotations. To demonstrate the scalability of our method, we generate nationwide maps of school location predictions in African countries and present a detailed analysis of our results, using Senegal as our case study. Finally, we demonstrate the immediate usability of our work by introducing an interactive web mapping tool to streamline human-in-the-loop model validation efforts by government partners. This work successfully showcases the real-world utility of deep learning and satellite images for planning regional infrastructure and accelerating universal school connectivity.