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.

翻译：地雷仍是持续存在的人道主义威胁，据估计全球60个国家部署了约1.1亿枚地雷，每年造成约2.6万人伤亡。现有探测方法具有危险性、低效性且成本高昂。本文提出自适应多光谱地雷识别数据集（AMLID），这是首个结合红绿蓝（RGB）与长波红外（LWIR）影像的开源数据集，专为基于无人机系统（UAS）的地雷探测而构建。AMLID包含12,078张标注图像，涵盖21种全球部署的地雷类型，涉及杀伤人员与反坦克两类，材质包含金属与塑料。数据集涵盖11种RGB-LWIR融合层级、四种传感器高度、两个季节周期及三种日间光照条件。通过提供跨多样环境变量的全面多光谱覆盖，AMLID使研究人员能够在不接触实弹或昂贵数据采集基础设施的情况下，开发并基准化自适应检测算法，从而推动人道主义排雷研究的普及化发展。