A Multi-tiered Human-in-the-loop Approach for Interactive School Mapping Using Earth Observation and Machine Learning

TL;DR

This paper introduces a multi-tiered human-in-the-loop framework combining machine learning and satellite imagery to improve school mapping accuracy in developing regions, enabling scalable and cost-effective educational infrastructure data collection.

Contribution

The paper proposes a novel multi-tiered approach integrating various satellite resolutions and human validation to enhance school location detection in data-scarce areas.

Findings

The approach improves mapping accuracy over traditional methods.

Human-in-the-loop validation enhances reliability of results.

Medium-resolution analysis was found less effective than high-resolution imagery.

Abstract

This paper presents a multi-tiered human-in-the-loop framework for interactive school mapping designed to improve the accuracy and completeness of educational facility records, particularly in developing regions where such data may be scarce and infrequently updated. The first tier involves a machine learning based analysis of population density, land cover, and existing infrastructure compared with known school locations. The first tier identifies potential gaps and "mislabelled" schools. In subsequent tiers, medium-resolution satellite imagery (Sentinel-2) is investigated to pinpoint regions with a high likelihood of school presence, followed by the application of very high-resolution (VHR) imagery and deep learning models to generate detailed candidate locations for schools within these prioritised areas. The medium-resolution approach was later removed due to insignificant improvements. The medium and VHR resolution models build upon global pre-trained steps to improve generalisation. A key component of the proposed approach is an interactive interface to allow human operators to iteratively review, validate, and refine the mapping results. Preliminary evaluations indicate that the multi-tiered strategy provides a scalable and cost-effective solution for educational infrastructure mapping to support planning and resource allocation.