# Vehicle Re-identification in Aerial Imagery: Dataset and Approach

arXiv: https://arxiv.org/abs/1904.01400 · HTML: https://mlsanity.com/paper/1904.01400/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 25 of 25)

- [S2Looking: A Satellite Side-Looking Dataset for Building Change Detection](https://mlsanity.com/paper/2107.09244/) (2021)
- [Transformer for Object Re-Identification: A Survey](https://mlsanity.com/paper/2401.06960/) (2024)
- [BuckTales : A multi-UAV dataset for multi-object tracking and re-identification of wild antelopes](https://mlsanity.com/paper/2411.06896/) (2024)
- [Rotation Invariant Transformer for Recognizing Object in UAVs](https://mlsanity.com/paper/2311.02559/) (2023)
- [Text-based Person Search in Full Images via Semantic-Driven Proposal Generation](https://mlsanity.com/paper/2109.12965/) (2021)
- [Ground-to-Aerial Person Search: Benchmark Dataset and Approach](https://mlsanity.com/paper/2308.12712/) (2023)
- [Enhancing Visible-Infrared Person Re-identification with Modality- and Instance-aware Visual Prompt Learning](https://mlsanity.com/paper/2406.12316/) (2024)
- [An Empirical Study of Vehicle Re-Identification on the AI City Challenge](https://mlsanity.com/paper/2105.09701/) (2021)
- [Uncertainty Aware Multitask Pyramid Vision Transformer For UAV-Based Object Re-Identification](https://mlsanity.com/paper/2209.08686/) (2022)
- [UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking](https://mlsanity.com/paper/2409.03245/) (2024)
- [AirSpatialBot: A Spatially-Aware Aerial Agent for Fine-Grained Vehicle Attribute Recognization and Retrieval](https://mlsanity.com/paper/2601.01416/) (2026)
- [Instance and Pair-Aware Dynamic Networks for Re-Identification](https://mlsanity.com/paper/2103.05395/) (2021)
- [Low-Power Multi-Camera Object Re-Identification using Hierarchical Neural Networks](https://mlsanity.com/paper/2106.10588/) (2021)
- [Self-aligned Spatial Feature Extraction Network for UAV Vehicle Re-identification](https://mlsanity.com/paper/2201.02836/) (2022)
- [Pluggable Weakly-Supervised Cross-View Learning for Accurate Vehicle Re-Identification](https://mlsanity.com/paper/2103.05376/) (2021)
- [SiamReID: Confuser Aware Siamese Tracker with Re-identification Feature](https://mlsanity.com/paper/2104.03510/) (2021)
- [Semi-supervised Ranking for Object Image Blur Assessment](https://mlsanity.com/paper/2207.06085/) (2022)
- [Semi-automatic Data Annotation System for Multi-Target Multi-Camera Vehicle Tracking](https://mlsanity.com/paper/2209.09606/) (2022)
- [A Novel Dual-pooling Attention Module for UAV Vehicle Re-identification](https://mlsanity.com/paper/2306.14104/) (2023)
- [Beyond Sharing Weights in Decoupling Feature Learning Network for UAV RGB-Infrared Vehicle Re-Identification](https://mlsanity.com/paper/2310.08026/) (2023)
- [CORE-ReID V2: Advancing the Domain Adaptation for Object Re-Identification with Optimized Training and Ensemble Fusion](https://mlsanity.com/paper/2508.04036/) (2025)
- [Rectifying Geometry-Induced Similarity Distortions for Real-World Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2601.21405/) (2026)
- [PCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning Systems](https://mlsanity.com/paper/2605.16098/) (2026)
- [Benchmarking UAV-based Vehicle Re-Identification under Simulated Weather Conditions](https://mlsanity.com/paper/2607.10583/) (2026)
- [Beyond Appearance: A Multi-cue Framework and Large-scale Benchmark for Pedestrian Association and Tracking on Mobile Aerial-Ground Platforms](https://mlsanity.com/paper/2607.23803/) (2026)

## Abstract (arXiv)

In this work, we construct a large-scale dataset for vehicle re-identification (ReID), which contains 137k images of 13k vehicle instances captured by UAV-mounted cameras. To our knowledge, it is the largest UAV-based vehicle ReID dataset. To increase intra-class variation, each vehicle is captured by at least two UAVs at different locations, with diverse view-angles and flight-altitudes. We manually label a variety of vehicle attributes, including vehicle type, color, skylight, bumper, spare tire and luggage rack. Furthermore, for each vehicle image, the annotator is also required to mark the discriminative parts that helps them to distinguish this particular vehicle from others. Besides the dataset, we also design a specific vehicle ReID algorithm to make full use of the rich annotation information. It is capable of explicitly detecting discriminative parts for each specific vehicle and significantly outperforms the evaluated baselines and state-of-the-art vehicle ReID approaches.