Drone-view target localization

Contains data from three platforms, i.e., synthetic drones, satellites and ground cameras of 1,652 university buildings around the world. University-1652 is a drone-based geo-localization dataset and enables two new tasks, i.e., drone-view target localization and drone navigation. Source: University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization

The first and large scale dataset to solve multi-object tracking and Re-identification problem with wild animals using UAVs. The dataset provides annotations for object detection, multi-object tracking and re-identification. Object detection dataset is specific for MOT problem : 320 Images MOT : 12 videos (21K frames) of 5.4K resolution, avg length 66 sec (Total : 1.2 Million) Longest video in MOT is 3 min (5800) Each video has 30-130 animals with different types of clusters and fast/slow movements. Total tracks : 680 Re-Identification (video) : 730 Individuals records from two different drones simultaneuously in 11 video pairs. Video length = 600 - 1100 frames.

GTA-UAV dataset provides a large continuous area dataset (covering 81.3km²) for UAV visual geo-localization, expanding the previously aligned drone-satellite pairs to arbitrary drone-satellite pairs to better align with real-world application scenarios. Our dataset contains: - 33,763 simulated drone-view images, from multiple altitudes (80-650m), multiple attitudes, multiple scenes (urban, mountain, coast, forest, etc.). - 14,640 tiled satellite-view images from 4 zoom levels for arbitrarily pairing. - Overlap (in IoU) of FoV for each drone-satellite pair. - Drone (camera) 6-DoF labels for each drone image.

Applications of unmanned aerial vehicle (UAV) in logistics, agricultural automation, urban management, and emergency response are highly dependent on oriented object detection (OOD) to enhance visual perception. Although existing datasets for OOD in UAV provide valuable resources, they are often designed for specific downstream tasks. Consequently, they exhibit limited generalization performance in real flight scenarios and fail to thoroughly demonstrate algorithm effectiveness in practical environments. To bridge this critical gap, we introduce CODrone, a comprehensive oriented object detection dataset for UAVs that accurately reflects real-world conditions. It also serves as a new benchmark designed to align with downstream task requirements, ensuring greater applicability and robustness in UAV-based OOD. Based on application requirements, we identify four key limitations in current UAV OOD datasets-low image resolution, limited object categories, single-view imaging, and restricted flight altitudes-and propose corresponding improvements to enhance their applicability and robustness. Furthermore, CODrone contains a broad spectrum of annotated images collected from multiple cities under various lighting conditions, enhancing the realism of the benchmark. To rigorously evaluate CODrone as a new benchmark and gain deeper insights into the novel challenges it presents, we conduct a series of experiments based on 22 classical or SOTA methods. Our evaluation not only assesses the effectiveness of CODrone in real-world scenarios but also highlights key bottlenecks and opportunities to advance OOD in UAV applications. Overall, CODrone fills the data gap in OOD from UAV perspective and provides a benchmark with enhanced generalization capability, better aligning with practical applications and future algorithm development. The dataset has been publicly released and is available at: \url{https://github.com/AHideoKuzeA/CODrone-A-Comprehensive-Oriented-Object-Detection-benchmark-for-UAV}.