Geosystems Research Institute, Mississippi State University, 2 Research Blvd, Starkville, MS 39759, United States.
Computer Sciences and Computer Engineering, University of Southern Mississippi, 118 College Drive, Hattiesburg, MS 39406, United States.
Database (Oxford). 2024 Jul 23;2024. doi: 10.1093/database/baae070.
Drones (unoccupied aircraft systems) have become effective tools for wildlife monitoring and conservation. Automated animal detection and classification using artificial intelligence (AI) can substantially reduce logistical and financial costs and improve drone surveys. However, the lack of annotated animal imagery for training AI is a critical bottleneck in achieving accurate performance of AI algorithms compared to other fields. To bridge this gap for drone imagery and help advance and standardize automated animal classification, we have created the Aerial Wildlife Image Repository (AWIR), which is a dynamic, interactive database with annotated images captured from drone platforms using visible and thermal cameras. The AWIR provides the first open-access repository for users to upload, annotate, and curate images of animals acquired from drones. The AWIR also provides annotated imagery and benchmark datasets that users can download to train AI algorithms to automatically detect and classify animals, and compare algorithm performance. The AWIR contains 6587 animal objects in 1325 visible and thermal drone images of predominantly large birds and mammals of 13 species in open areas of North America. As contributors increase the taxonomic and geographic diversity of available images, the AWIR will open future avenues for AI research to improve animal surveys using drones for conservation applications. Database URL: https://projectportal.gri.msstate.edu/awir/.
无人机（无人飞行器系统）已成为野生动物监测和保护的有效工具。使用人工智能（AI）进行自动动物检测和分类可以大大降低后勤和财务成本，并提高无人机调查的效率。然而，缺乏用于训练 AI 的标注动物图像是实现 AI 算法与其他领域相比达到准确性能的关键瓶颈。为了弥合这一差距，推进和标准化自动化动物分类，我们创建了空中野生动物图像库（AWIR），这是一个具有注释图像的动态、交互式数据库，这些图像是使用可见和热摄像机从无人机平台上捕获的。AWIR 为用户提供了第一个开放访问的存储库，用于上传、注释和管理从无人机获取的动物图像。AWIR 还提供了标注图像和基准数据集，用户可以下载这些数据集来训练 AI 算法，以自动检测和分类动物，并比较算法性能。AWIR 包含 1325 张可见光和热无人机图像中的 6587 个动物对象，这些图像主要拍摄了北美洲开阔地区的 13 种大型鸟类和哺乳动物。随着贡献者增加可用图像的分类和地理多样性，AWIR 将为使用无人机进行保护应用的动物调查的 AI 研究开辟新的途径。数据库网址：https://projectportal.gri.msstate.edu/awir/。
