The BAMBI Dataset: Multimodal Nadir UAV-Recordings of Forest Wildlife

Large-scale wildlife monitoring in forested environments using drones remains challenging due to occlusion, limited visibility, and scarce annotated data. We present a comprehensive airborne wildlife dataset comprising 386 paired RGB and thermal aerial video sequences recorded across diverse temperate forests and forest-adjacent habitats in Austria. Each frame is geo-referenced with precise global coordinates (longitude, latitude and altitude), enabling learning and evaluation in both image space and geographic space. The dataset includes 10 animal species captured under varying seasonal, environmental, and illumination conditions, with annotations supporting tasks such as object detection, multi-object tracking and fine-grained classification. The multispectral data and spatial metadata enable research on world-coordinate trajectory analysis, spatial population modeling, geo-aware perception, and advanced methods such as Airborne Light Field Sampling. Thermal subsets of this dataset have been used to develop and validate methods for wildlife monitoring, while the RGB data has mainly stayed untouched. To address this, we present a cross-modal pipeline to transfer thermal annotations to RGB frames next to the dataset. With this dataset we aim to provide a blueprint to promote research on multimodal, geo-referenced perception in ecology.