用卫星图像自动识别学校和信号塔，帮偏远地区规划网络覆盖。
Signal from Space: Detecting Schools and Towers to Bridge the Digital Divide
- 仅用高分辨率卫星图和迁移学习，同时检测学校与信号塔。
- 在莱索托实测中表现良好，能有效评估网络覆盖潜力。
- 适合国际组织与政府用于精准制定网络基建优先级。
可靠的互联网接入对现代教育至关重要，但许多发展中国家的学龄儿童仍因学校未联网而无法上网。联合国教科文组织发起的Giga计划旨在连接每一所学校，但大规模实施需高效方法来绘制学校位置并评估周边通信基础设施，且不依赖稀疏或嘈杂的第三方数据。本文提出一种可扩展的纯视觉框架，利用高分辨率卫星影像与迁移学习，同时完成学校与基站的检测任务。通过将预训练目标检测模型适配至新地理区域，仅需少量标注数据即可实现从太空直接识别学校与蜂窝信号塔。随后分析二者空间关系，作为连通性可用性的代理指标。该纯图像驱动流程支持大规模基础设施测绘，降低对外部数据依赖，并助力数据驱动的欠发达地区网络投资优先级决策。方法在莱索托真实卫星影像上验证，表现出色。
原文摘要 · Abstract (English)
Reliable internet access is essential for modern education, yet millions of school-aged children especially in developing regions remain offline due to unconnected schools. The Giga Initiative aims to connect every school to the internet, but doing so at scale requires efficient methods to map schools and assess surrounding connectivity infrastructure without relying on sparse or noisy third-party datasets. In this work, we propose a scalable, vision-only framework that uses high-resolution satellite imagery and transfer learning to address both tasks simultaneously. By adapting pre-trained object detection models to new geographical regions with minimal labeled data, we detect schools and cell towers directly from space. We then analyze the spatial relationship between detected schools and nearby towers as a proxy for connectivity availability. This purely imagery-driven pipeline enables large-scale infrastructure mapping, reduces dependency on auxiliary data, and supports data-driven prioritization of connectivity investments in underserved areas. Our approach is demonstrated on real satellite imagery from Lesotho, showing strong performance across this region.
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