# UAVD4L: A Large-Scale Dataset for UAV 6-DoF Localization

arXiv: https://arxiv.org/abs/2401.05971 · HTML: https://mlsanity.com/paper/2401.05971/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 54 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 27 of 27)

- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Back to the Feature: Learning Robust Camera Localization from Pixels to Pose](https://mlsanity.com/paper/2103.09213/) (2021)
- [University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization](https://mlsanity.com/paper/2002.12186/) (2020)
- [End-to-end Learning of Deep Visual Representations for Image Retrieval](https://mlsanity.com/paper/1610.07940/) (2016)
- [LENS: Localization enhanced by NeRF synthesis](https://mlsanity.com/paper/2110.06558/) (2021)
- [Self-supervising Fine-grained Region Similarities for Large-scale Image Localization](https://mlsanity.com/paper/2006.03926/) (2020)
- [LaMAR: Benchmarking Localization and Mapping for Augmented Reality](https://mlsanity.com/paper/2210.10770/) (2022)
- [MeshLoc: Mesh-Based Visual Localization](https://mlsanity.com/paper/2207.10762/) (2022)
- [CrossLoc: Scalable Aerial Localization Assisted by Multimodal Synthetic Data](https://mlsanity.com/paper/2112.09081/) (2021)
- [UAV Pose Estimation using Cross-view Geolocalization with Satellite Imagery](https://mlsanity.com/paper/1809.05979/) (2018)
- [Are Local Features All You Need for Cross-Domain Visual Place Recognition?](https://mlsanity.com/paper/2304.05887/) (2023)
- [UAV Localization Using Autoencoded Satellite Images](https://mlsanity.com/paper/2102.05692/) (2021)
- [i3dLoc: Image-to-range Cross-domain Localization Robust to Inconsistent Environmental Conditions](https://mlsanity.com/paper/2105.12883/) (2021)
- [Long-term Visual Localization with Mobile Sensors](https://mlsanity.com/paper/2304.07691/) (2023)
- [ALTO: A Large-Scale Dataset for UAV Visual Place Recognition and Localization](https://mlsanity.com/paper/2207.12317/) (2022)
- [Real-time Geo-localization Using Satellite Imagery and Topography for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2108.03344/) (2021)
- [iSimLoc: Visual Global Localization for Previously Unseen Environments with Simulated Images](https://mlsanity.com/paper/2209.06376/) (2022)
- [BRM Localization: UAV Localization in GNSS-Denied Environments Based on Matching of Numerical Map and UAV Images](https://mlsanity.com/paper/2008.01347/) (2020)
- [Vision-based GNSS-Free Localization for UAVs in the Wild](https://mlsanity.com/paper/2210.09727/) (2022)
- [A Multi-Stage Multi-Task Neural Network for Aerial Scene Interpretation and Geolocalization](https://mlsanity.com/paper/1804.01322/) (2018)
- [GNSS-denied geolocalization of UAVs by visual matching of onboard camera images with orthophotos](https://mlsanity.com/paper/2103.14381/) (2021)
- [RenderNet: Visual Relocalization Using Virtual Viewpoints in Large-Scale Indoor Environments](https://mlsanity.com/paper/2207.12579/) (2022)
- [LSVL: Large-scale season-invariant visual localization for UAVs](https://mlsanity.com/paper/2212.03581/) (2022)

## Built on by (most cited first, 22 of 22)

- [UAVScenes: A Multi-Modal Dataset for UAVs](https://mlsanity.com/paper/2507.22412/) (2025)
- [LoD-Loc: Aerial Visual Localization using LoD 3D Map with Neural Wireframe Alignment](https://mlsanity.com/paper/2410.12269/) (2024)
- [OrthoLoC: UAV 6-DoF Localization and Calibration Using Orthographic Geodata](https://mlsanity.com/paper/2509.18350/) (2025)
- [LoD-Loc v2: Aerial Visual Localization over Low Level-of-Detail City Models using Explicit Silhouette Alignment](https://mlsanity.com/paper/2507.00659/) (2025)
- [OccuFly: A 3D Vision Benchmark for Semantic Scene Completion from the Aerial Perspective](https://mlsanity.com/paper/2512.20770/) (2025)
- [NTR-Gaussian: Nighttime Dynamic Thermal Reconstruction with 4D Gaussian Splatting Based on Thermodynamics](https://mlsanity.com/paper/2503.03115/) (2025)
- [AirZoo: A Unified Large-Scale Dataset for Grounding Aerial Geometric 3D Vision](https://mlsanity.com/paper/2604.26567/) (2026)
- [AerialMetric: Benchmarking and Adapting UAV Monocular Metric Depth Estimation in the Real World](https://mlsanity.com/paper/2606.29716/) (2026)
- [Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approaches](https://mlsanity.com/paper/2507.14633/) (2025)
- [ABot-Earth 0.5: Generative 3D Earth Model](https://mlsanity.com/paper/2606.09967/) (2026)
- [UAVDB: Point-Guided Masks for UAV Detection and Segmentation](https://mlsanity.com/paper/2409.06490/) (2024)
- [AerialGo: Walking-through City View Generation from Aerial Perspectives](https://mlsanity.com/paper/2412.00157/) (2024)
- [SWA-PF: Semantic-Weighted Adaptive Particle Filter for Memory-Efficient 4-DoF UAV Localization in GNSS-Denied Environments](https://mlsanity.com/paper/2509.13795/) (2025)
- [PiLoT: Neural Pixel-to-3D Registration for UAV-based Ego and Target Geo-localization](https://mlsanity.com/paper/2603.20778/) (2026)
- [egenioussBench: A New Dataset for Geospatial Visual Localisation](https://mlsanity.com/paper/2605.05351/) (2026)
- [OrthoTrack: Continuous 6-DoF UAV Trajectory Estimation Anchored in Public Orthophotos](https://mlsanity.com/paper/2606.25245/) (2026)
- [Beyond a Single Light: A Large-Scale Aerial Dataset for Urban Scene Reconstruction Under Varying Illumination](https://mlsanity.com/paper/2512.14200/) (2025)
- [SOAR: Regression-based LiDAR Relocalization for UAVs](https://mlsanity.com/paper/2602.13267/) (2026)
- [LoD-Loc v3: Generalized Aerial Localization in Dense Cities using Instance Silhouette Alignment](https://mlsanity.com/paper/2603.19609/) (2026)
- [SemCityLoc: Aerial 6DoF Localization Using Semantic 3D City Models](https://mlsanity.com/paper/2606.27444/) (2026)
- [PiLoT v2: Pixel-to-Orthogonal Map Alignment for Free-view UAV Geo-localization](https://mlsanity.com/paper/2606.31098/) (2026)
- [ScalLoc: Continuous Oblique Thermal UAV Geo-Localization with a 2D Satellite Map](https://mlsanity.com/paper/2610.14866/) (2026)

## Abstract (arXiv)

Despite significant progress in global localization of Unmanned Aerial Vehicles (UAVs) in GPS-denied environments, existing methods remain constrained by the availability of datasets. Current datasets often focus on small-scale scenes and lack viewpoint variability, accurate ground truth (GT) pose, and UAV build-in sensor data. To address these limitations, we introduce a large-scale 6-DoF UAV dataset for localization (UAVD4L) and develop a two-stage 6-DoF localization pipeline (UAVLoc), which consists of offline synthetic data generation and online visual localization. Additionally, based on the 6-DoF estimator, we design a hierarchical system for tracking ground target in 3D space. Experimental results on the new dataset demonstrate the effectiveness of the proposed approach. Code and dataset are available at https://github.com/RingoWRW/UAVD4L