Machine Learning for ICT infrastructure detection

Machine Learning for ICT infrastructure detection

Sep 15, 2026 · Time to be announced

This session introduces how ITU/BDT/DNE/FNS is applying machine learning (computer vision) to improve the availability and quality of ICT infrastructure data by detecting telecommunication towers from satellite imagery. Participants will see the end-to-end workflow (data labeling → model training → inference), learn how the work is being advanced with partners (including KAUST), and discuss how the resulting data and capacity-building approach can strengthen ITU/BDT connectivity programmes and country support. Session ObjectivesBy the end of this session, participants will be able to:A clear understanding of the problem in getting global connectivity, addressing: gaps in reliable tower/infrastructure data for network planning and gap analysis.A practical view of the tower-detection pipeline: image collection and annotation, ML model training, and inference to extract tower locations.Awareness of the supporting tools developed for demonstration and training (labeling tool and tower detector app) and how they can be used in capacity-building contexts.An overview of how partnerships (e.g., KAUST) contribute to model improvement, scaling, and knowledge transfer.A shared discussion on how improved infrastructure data and AI-enabled workflows can feed into ITU/BDT programmes (planning, measurement, and implementation support) and where to pilot next.

Session status: scheduled

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