Enabling Decision-Support Systems through Automated Cell Tower Detection
This work addresses mobile coverage gaps in rural sub-Saharan Africa, enabling improved digital service delivery, but it is incremental as it applies existing object detection methods to a new domain-specific dataset.
The study tackled the problem of mapping cell towers in rural sub-Saharan Africa to address mobile coverage gaps by developing a partially automated workflow using deep neural networks and remote sensing imagery, achieving an average precision of 81.2% at 50% IoU on a dataset of over 6,000 images across 26 countries.
Cell phone coverage and high-speed service gaps persist in rural areas in sub-Saharan Africa, impacting public access to mobile-based financial, educational, and humanitarian services. Improving maps of telecommunications infrastructure can help inform strategies to eliminate gaps in mobile coverage. Deep neural networks, paired with remote sensing images, can be used for object detection of cell towers and eliminate the need for inefficient and burdensome manual mapping to find objects over large geographic regions. In this study, we demonstrate a partially automated workflow to train an object detection model to locate cell towers using OpenStreetMap (OSM) features and high-resolution Maxar imagery. For model fine-tuning and evaluation, we curated a diverse dataset of over 6,000 unique images of cell towers in 26 countries in eastern, southern, and central Africa using automatically generated annotations from OSM points. Our model achieves an average precision at 50% Intersection over Union (IoU) (AP@50) of 81.2 with good performance across different geographies and out-of-sample testing. Accurate localization of cell towers can yield more accurate cell coverage maps, in turn enabling improved delivery of digital services for decision-support applications.