AMLID: An Adaptive Multispectral Landmine Identification Dataset for Drone-Based Detection
This addresses the humanitarian threat of landmines by providing an open-source dataset for drone-based detection research, though it is incremental as it focuses on data rather than a new detection method.
The authors tackled the problem of landmine detection by creating the Adaptive Multispectral Landmine Identification Dataset (AMLID), which includes 12,078 labeled images with RGB and LWIR imagery across diverse conditions, enabling algorithm development without live ordnance.
Landmines remain a persistent humanitarian threat, with an estimated 110 million mines deployed across 60 countries, claiming approximately 26,000 casualties annually. Current detection methods are hazardous, inefficient, and prohibitively expensive. We present the Adaptive Multispectral Landmine Identification Dataset (AMLID), the first open-source dataset combining Red-Green-Blue (RGB) and Long-Wave Infrared (LWIR) imagery for Unmanned Aerial Systems (UAS)-based landmine detection. AMLID comprises of 12,078 labeled images featuring 21 globally deployed landmine types across anti-personnel and anti-tank categories in both metal and plastic compositions. The dataset spans 11 RGB-LWIR fusion levels, four sensor altitudes, two seasonal periods, and three daily illumination conditions. By providing comprehensive multispectral coverage across diverse environmental variables, AMLID enables researchers to develop and benchmark adaptive detection algorithms without requiring access to live ordnance or expensive data collection infrastructure, thereby democratizing humanitarian demining research.