Country-Wide Digital Twin for Humanitarian AI: The Case Study of Cambodian Mine Action

Gürkan Solmaz, Flavio Cirillo, Sébastien Pierrel

Keywords: environmental modeling, digital twins, data enrichment

TL;DR: This paper presents a country-wide Digital Twin for landmine risk assessment, deployed in Cambodia, that streamlines ML workflows and achieves high accuracy in identifying high-risk areas.

Abstract: Landmine contamination poses a significant threat in post-conflict regions, endangering civilian populations even decades after hostilities end. Demining efforts are resource-intensive, and prioritizing areas for clearance is particularly challenging in regions with widespread contamination. This paper presents a country-wide Digital Twin for landmine risk assessment, addressing key challenges: (a) modeling of a country-wide Digital Twin, (b) data integration, enrichment and augmentation, and (c) efficient machine learning (ML) training. The system is deployed in Cambodia premises, where it streamlines the development of ML workflows and achieves up to 91% accuracy in assessing risks of landmine areas.

Submission Number: 4