统一无人机野生动物数据标准，促进多领域协同研究
FAIR^2 Drones: An AI-Ready Standard for Cross-Domain Wildlife Drone Datasets
- 建立跨领域的无人机数据标准，整合生态、机器人与视觉需求
- 支持多模态数据融合，含影像、GPS、声学等互补信息
- 适合生态监测、智能算法开发等跨学科研究人员使用
利用无人机进行动物生态数据采集需投入大量时间、专业技能和资金。然而现有数据集大多仅服务于单一研究群体，限制了跨学科复用。我们提出统一的无人机数据集标准 FAIR^2 Drones，基于现有 FAIR 与 AI 友好数据框架，增加关键平台元数据与标注规范，使数据集可同时支持生态分析、机器人算法开发与计算机视觉基准测试。提供开源验证工具、参考实现及多模态扩展，连接无人机影像与相机陷阱、GPS、声学等互补传感器数据。通过标准化跨学科元数据，最大化昂贵野外部署的科研回报，加速环境监测中的跨领域协作。
原文摘要 · Abstract (English)
Animal ecology data collection using drones represents a substantial investment of time, expertise, and financial resources. Yet most existing datasets serve only a single research community, limiting interdisciplinary reuse. We propose a unified drone dataset standard, FAIR^2 Drones, that bridges ecology, robotics, and computer vision by building on existing FAIR and AI-ready data frameworks while adding essential platform metadata and annotation specifications. Our standard enables datasets to simultaneously support ecological analysis, robotics algorithm development, and computer vision benchmarking. We provide open-source validation tools, reference implementations, and multimodal extensions linking drone imagery with complementary sensors such as camera traps, GPS, and acoustics. By standardizing metadata across disciplines, this framework maximizes the scientific return on investment for costly field deployments and accelerates cross-domain collaboration in environmental monitoring.
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