Overcoming operational barriers to scalable school detection using low-cost satellite imagery and cross-country knowledge distillation

Pattichis, Andreas

Overcoming operational barriers to scalable school detection using low-cost satellite imagery and cross-country knowledge distillation

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Citation

Pattichis, A. Overcoming operational barriers to scalable school detection using low-cost satellite imagery and cross-country knowledge distillation. 2025. handle: http://hdl.handle.net/10230/71766

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Abstract

Over 2.2 billion children lack internet access at home. To address this critical gap, UNICEF and ITU’s Giga initiative aims to connect every school to the internet by 2030 using AI-powered school mapping that relies on satellite imagery. However, Giga’s existing weakly supervised deep learning workflow faces significant challenges when scaling to low- and middle-income countries. The pipeline uses expensive high-resolution Maxar satellite imagery. Moreover, preparing each country’s training dataset demands extensive manual validation, often requiring several days of expert time. To overcome these limitations, this research extends the established pipeline through three successive approaches for end-to-end improvement. First, we evaluate Mapbox as a lower-cost imagery alternative. Second, we build cross country ensembles that eliminate target-country training requirements by leveraging neighboring-country models. Third, we distill knowledge from these ensembles to reduce manual validation requirements by automatically generating soft labels for target-country student models. Our experimental evaluation across three SubSaharan African countries reveals three key findings. Mapbox imagery maintains performance within 10 % of Maxar baselines, providing a cost-effective alternative. Cross-country ensembles achieve comparable performance to target-country baselines while eliminating the need for target-country labels. Most significantly, Knowledge Distillation nearly matches target-country baseline performance, outperforms cross-country ensembles, and auto-generates soft labels for 82–88 % of school samples reducing manual validation from days to hours. These findings establish a practical framework that cuts the cost and expertise needed for AI-driven school mapping, marking substantial progress toward sustainable local ownership and Giga’s 2030 goal of universal school connectivity.

Description

Treball fi de màster de: Erasmus Mundus joint Master in Artificial Intelligence (EMAI)
Supervisor: Dr. Ivan Dotu Co-Supervisor: Professor Vicenç Gómez