RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization
This work addresses the critical issue of landmine detection for war-affected communities, offering a practical tool for demining organizations, though it appears incremental as it builds on existing risk modeling and interpretable methods.
The paper tackles the problem of landmine risk estimation for humanitarian demining by proposing the RELand system, which includes feature engineering guidelines, an interpretable classification model using sparse feature masking and invariant risk minimization, and an interactive web interface, showing significant improvement over state-of-the-art methods in evaluations that mimic real-world operations.
Landmines remain a threat to war-affected communities for years after conflicts have ended, partly due to the laborious nature of demining tasks. Humanitarian demining operations begin by collecting relevant information from the sites to be cleared, which is then analyzed by human experts to determine the potential risk of remaining landmines. In this paper, we propose RELand system to support these tasks, which consists of three major components. We (1) provide general feature engineering and label assigning guidelines to enhance datasets for landmine risk modeling, which are widely applicable to global demining routines, (2) formulate landmine presence as a classification problem and design a novel interpretable model based on sparse feature masking and invariant risk minimization, and run extensive evaluation under proper protocols that resemble real-world demining operations to show a significant improvement over the state-of-the-art, and (3) build an interactive web interface to suggest priority areas for demining organizations. We are currently collaborating with a humanitarian demining NGO in Colombia that is using our system as part of their field operations in two areas recently prioritized for demining.