Enabling Decision-Support Systems through Automated Cell Tower Detection
Natasha Krell Will Gleave Daniel Nakada Justin Downes Amanda Willet Matthew Baran
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.