Dataset Hub

The VRAI dataset supports vehicle re‑identification research in aerial imagery. With the rapid growth of unmanned aerial vehicles (UAVs), UAV‑based visual applications have attracted increasing attention from both industry and academia. However, public UAV vehicle re‑ID datasets are scarce, limiting research despite potential applications such as long‑term tracking and visual object retrieval. VRAI addresses this gap by providing a large‑scale, publicly available collection of UAV‑captured vehicle images with comprehensive annotations.

HazyDet is a large‑scale dataset created by the PLA Engineering University and other institutions, specifically for drone‑view object detection in haze and smog conditions. It contains 383,000 real‑world instances collected from natural haze environments and normal scenes where haze effects were artificially added to simulate adverse weather. The dataset creation combined depth estimation and atmospheric scattering models to ensure realism and diversity. HazyDet is primarily applied to object detection for drones operating under harsh weather, aiming to enhance drone perception in complex environments.

This is a UAV dataset provided by the EMS Group at TU Ilmenau for radar and emitter localization testing; the dataset files are not stored directly in this repository but can be downloaded via the `downloader.py` script.

This repository contains a subset of the Nordland dataset used for the FlyNet project. Additional extended subsets may be added in the future for convenience.

AU‑AIR is a multimodal UAV aerial dataset that includes visual data, object annotations, and flight telemetry (time, GPS, altitude, IMU sensor data, velocity), suitable for UAV vision and robotics research.

The HUVER dataset contains 6,051 unique drone configurations, each described by multiple formats such as grammar strings, RGB images, and GLB files. Additionally, each configuration includes an English textual descriptor that details the drone’s features in natural language. The dataset supports tasks such as image‑to‑text, image‑to‑3D, and feature extraction, and is curated by Abhiram Karri, Gary Stump, Christopher McComb, and Binyang Song under the MIT License.