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Landmines remain a threat to war-affected communities for years afterconflicts have ended, partly due to the laborious nature of demining tasks.Humanitarian demining operations begin by collecting relevant information fromthe sites to be cleared, which is then analyzed by human experts to determinethe potential risk of remaining landmines. In this paper, we propose RELandsystem to support these tasks, which consists of three major components. We (1)provide general feature engineering and label assigning guidelines to enhancedatasets for landmine risk modeling, which are widely applicable to globaldemining routines, (2) formulate landmine presence as a classification problemand design a novel interpretable model based on sparse feature masking andinvariant risk minimization, and run extensive evaluation under properprotocols that resemble real-world demining operations to show a significantimprovement over the state-of-the-art, and (3) build an interactive webinterface to suggest priority areas for demining organizations. We arecurrently collaborating with a humanitarian demining NGO in Colombia that isusing our system as part of their field operations in two areas recentlyprioritized for demining.

RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization