Identification of Hazardous Areas for Priority Landmine Clearance: AI for Humanitarian Mine Action
ML @ CMU
NOVEMBER 7, 2024
In close collaboration with the UN and local NGOs, we co-develop an interpretable predictive tool for landmine contamination to identify hazardous clusters under geographic and budget constraints, experimentally reducing false alarms and clearance time by half. RELand consistently outperforms the benchmark models on all relevant metrics.
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