用多层人机协作提升发展中国家学校地图精度与完整性。
A Multi-tiered Human-in-the-loop Approach for Interactive School Mapping Using Earth Observation and Machine Learning
- 分层结合机器学习与卫星影像，逐步筛选潜在校址。
- 高分辨率影像与深度模型定位候选学校，准确率显著提升。
- 交互界面支持人工审核，适合教育规划者使用。
本文提出一种多层级人机协同的交互式学校制图框架，旨在提升发展中国家教育设施数据的准确性与完整性。第一层通过机器学习分析人口密度、土地覆盖和现有基础设施，对比已知校址识别出潜在遗漏或误标学校。随后利用中分辨率遥感影像（Sentinel-2）定位高概率建校区域，再结合超高分辨率影像与预训练深度学习模型，在优先区域生成详细候选校址。中分辨率方法因改进效果不显著被移除。模型基于全球预训练权重以增强泛化能力。核心是交互式界面，供人工迭代审查与修正结果。初步评估表明，该策略在成本与可扩展性上表现优异，有助于教育规划与资源分配。
原文摘要 · Abstract (English)
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.
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