RELand: Risk Estimation of Landmines via Interpretable Invariant Risk
  Minimization
Mateo Dulce Rubio Siqi Zeng Qi Wang Didier Alvarado Francisco Moreno Hoda Heidari Fei Fang
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.