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Landmine removal is a slow, resource-intensive process affecting over 60 countries. While AI has been proposed to enhance explosive ordnance (EO) detection, existing methods primarily focus on object recognition, with limited attention to prediction of landmine risk based on spatial pattern information. This work aims to answer the following research question: How can AI be used to predict landmine risk from landmine patterns to improve clearance time efficiency? To that effect, we introduce RestoreAI, an AI system for pattern-based risk estimation of remaining explosives. RestoreAI is the first AI system that leverages landmine patterns for risk prediction, improving the accuracy of estimating the residual risk of missing EO prior to land release. We particularly focus on the implementation of three instances of RestoreAI, respectively, linear, curved and Bayesian pattern deminers. First, the linear pattern deminer uses linear landmine patterns from a principal component analysis (PCA) for the landmine risk prediction. Second, the curved pattern deminer uses curved landmine patterns from principal curves. Finally, the Bayesian pattern deminer incorporates prior expert knowledge by using a Bayesian pattern risk prediction. Evaluated on real-world landmine data, RestoreAI significantly boosts clearance efficiency. The top-performing pattern-based deminers achieved a 14.37 percentage point increase in the average share of cleared landmines per timestep and required 24.45% less time than the best baseline deminer to locate all landmines. Interestingly, linear and curved pattern deminers showed no significant performance difference, suggesting that more efficient linear patterns are a viable option for risk prediction.

Cox's Bazar, Bangladesh – The sound of children at play echoes through the verdant lanes of one of the dozens of refugee camps on the outskirts of Cox's Bazar, a densely populated coastal town in southeast Bangladesh. Just for a moment, the sounds manage to soften the harsh living conditions faced by the more than one million people who live here in the world's largest refugee camp. Described as the most persecuted people on the planet, the Rohingya Muslim refugees in Bangladesh may now be one of the most forgotten populations in the world, eight years after being ethnically cleansed from their homes in neighbouring Myanmar by a predominantely Buddhist military regime. "Cox's Bazar is ground zero for the impact of budget cuts on people in desperate need," UN Secretary-General Antonio Guterres said during a visit to the sprawling camps in May. The UN chief's visit followed United States President Donald Trump's gutting of the US Agency for International Development (USAID), which has stalled several key projects in the camps, and the United Kingdom announcing cuts to foreign aid in order to increase defence spending.

TL;DR: Landmines pose a persistent threat and hinder development in over 70 war-affected countries. Humanitarian demining aims to clear contaminated areas, but progress is slow: at the current pace, it will take 1,100 years to fully demine the planet. 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. The system is being tested in Afghanistan and Colombia, where it has already led to the discovery of new landmines. Anti-personnel landmines are explosive devices hidden in the ground designed to explode by proximity or contact and with the capacity to kill, disable or cause harm to humans (Figure 1). The mere threat of landmine contamination in a territory not only endangers the physical well-being of affected populations but also results in a loss of forest areas, reduction of productive land, exacerbation of social vulnerability, delay of infrastructure development, and damage of natural, physical, and social capital.

Landmine detection using traditional methods is slow, dangerous and prohibitively expensive. Using deep learning-based object detection algorithms drone videos is promising but has multiple challenges due to the small, soda-can size of recently prevalent surface landmines. The literature currently lacks scientific evaluation of optimal ML models for this problem since most object detection research focuses on analysis of ground video surveillance images. In order to help train comprehensive models and drive research for surface landmine detection, we first create a custom dataset comprising drone images of POM-2 and POM-3 Russian surface landmines. Using this dataset, we train, test and compare 4 different computer vision foundation models YOLOF, DETR, Sparse-RCNN and VFNet. Generally, all 4 detectors do well with YOLOF outperforming other models with a mAP score of 0.89 while DETR, VFNET and Sparse-RCNN mAP scores are all around 0.82 for drone images taken from 10m AGL. YOLOF is also quicker to train consuming 56min of training time on a Nvidia V100 compute cluster. Finally, this research contributes landmine image, video datasets and model Jupyter notebooks at https://github.com/UnVeilX/ to enable future research in surface landmine detection.

The process of clearing areas, namely demining, starts by assessing and prioritizing potential hazardous areas (i.e., desk assessment) to go under thorough investigation of experts, who confirm the risk and proceed with the mines clearance operations. This paper presents Desk-AId that supports the desk assessment phase by estimating landmine risks using geospatial data and socioeconomic information. Desk-AId uses a Geospatial AI approach specialized to landmines. The approach includes mixed data sampling strategies and context-enrichment by historical conflicts and key multi-domain facilities (e.g., buildings, roads, health sites). The proposed system addresses the issue of having only ground-truth for confirmed hazardous areas by implementing a new hard-negative data sampling strategy, where negative points are sampled in the vicinity of hazardous areas. Experiments validate Desk-Aid in two domains for landmine risk assessment: 1) country-wide, and 2) uncharted study areas). The proposed approach increases the estimation accuracies up to 92%, for different classification models such as RandomForest (RF), Feedforward Neural Networks (FNN), and Graph Neural Networks (GNN).

Kyiv, Ukraine – Referring to the Soviet puzzle video game, Alla says her husband kills Russian soldiers as though he is playing "human Tetris". "A drone hangs in the sky, and he watches [them] crawl across the forest," she told Al Jazeera. And then they crawl again." The war in Ukraine has turned Avdiivka, where Alla's husband is stationed, into a maze of ruins, trenches and tunnels surrounded by burned-down fields and patches of forest studded with landmines, explosion craters and remnants of Russian soldiers and armoured vehicles. Avdiivka sits 20km (12 miles) north of separatist Donetsk, wedged deep into occupied areas.

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.

Landmine blasts can be fatal and cause injuries including blindness, burns, damaged limbs, and shrapnel wounds. While many nations have stopped using and producing landmines, 59 countries and territories remain contaminated by mines or other explosives. In 2019, landmines and similar explosives caused at least 5,554 casualties, across 55 countries and regions, with civilians accounting for the majority (80%) and children representing nearly half of civilian casualties (43%). Over one million landmines were dropped in Afghanistan in the 1980s. About two million landmines have been planted on the Korean Peninsula since the Korean War ended in 1953.

Convolutional Neural Networks are used for computer vision projects and can be used to automatically extract features from inputs like photos and videos. These neural networks employ so-called convolutional layers that convolve (slide) over the input image, try to detect patterns, and adapt weights accordingly during the training process -- allowing learning to occur. Sometimes, however, we want the opposite to happen: invert the output of a convolutional layer and reconstruct the original input. This is for example the case with autoencoders, where we use normal convolutions to learn an encoded state and subsequently decode them into the original inputs. If done successfully, the encoded state can be used as a lower-dimensional representation of our input data, for dimensionality reduction.