# UAV-VisLoc: A Large-scale Dataset for UAV Visual Localization

arXiv: https://arxiv.org/abs/2405.11936 · HTML: https://mlsanity.com/paper/2405.11936/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 20 May 2024.

## Builds on (most cited first, 5 of 5)

- [VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval](https://mlsanity.com/paper/2011.12172/) (2020)
- [Wide-Area Image Geolocalization with Aerial Reference Imagery](https://mlsanity.com/paper/1510.03743/) (2015)
- [University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization](https://mlsanity.com/paper/2002.12186/) (2020)
- [SUES-200: A Multi-height Multi-scene Cross-view Image Benchmark Across Drone and Satellite](https://mlsanity.com/paper/2204.10704/) (2022)
- [Jointly Optimized Global-Local Visual Localization of UAVs](https://mlsanity.com/paper/2310.08082/) (2023)

## Built on by (most cited first, 23 of 23)

- [UAVScenes: A Multi-Modal Dataset for UAVs](https://mlsanity.com/paper/2507.22412/) (2025)
- [Game4Loc: A UAV Geo-Localization Benchmark from Game Data](https://mlsanity.com/paper/2409.16925/) (2024)
- [OrthoLoC: UAV 6-DoF Localization and Calibration Using Orthographic Geodata](https://mlsanity.com/paper/2509.18350/) (2025)
- [Learning Dense Feature Matching via Lifting Single 2D Image to 3D Space](https://mlsanity.com/paper/2507.00392/) (2025)
- [Are VLMs Lost Between Sky and Space? LinkS2Bench for UAV-Satellite Dynamic Cross-View Spatial Intelligence](https://mlsanity.com/paper/2604.02020/) (2026)
- [AirZoo: A Unified Large-Scale Dataset for Grounding Aerial Geometric 3D Vision](https://mlsanity.com/paper/2604.26567/) (2026)
- [Hierarchical Image Matching for UAV Absolute Visual Localization via Semantic and Structural Constraints](https://mlsanity.com/paper/2506.09748/) (2025)
- [SCC-Loc: A Unified Semantic Cascade Consensus Framework for UAV Thermal Geo-Localization](https://mlsanity.com/paper/2604.03120/) (2026)
- [AerialGo: Walking-through City View Generation from Aerial Perspectives](https://mlsanity.com/paper/2412.00157/) (2024)
- [PiLoT: Neural Pixel-to-3D Registration for UAV-based Ego and Target Geo-localization](https://mlsanity.com/paper/2603.20778/) (2026)
- [OrthoTrack: Continuous 6-DoF UAV Trajectory Estimation Anchored in Public Orthophotos](https://mlsanity.com/paper/2606.25245/) (2026)
- [Visual Place Recognition for Large-Scale UAV Applications](https://mlsanity.com/paper/2507.15089/) (2025)
- [PAUL: Uncertainty-Guided Partition and Augmentation for Robust Cross-View Geo-Localization under Noisy Correspondence](https://mlsanity.com/paper/2508.20066/) (2025)
- [Beyond Paired Data: Self-Supervised UAV Geo-Localization from Reference Imagery Alone](https://mlsanity.com/paper/2512.02737/) (2025)
- [SOAR: Regression-based LiDAR Relocalization for UAVs](https://mlsanity.com/paper/2602.13267/) (2026)
- [Altitude-Adaptive Vision-Only Geo-Localization for UAVs in GPS-Denied Environments](https://mlsanity.com/paper/2602.23872/) (2026)
- [Seeing Across Skies and Streets: Feedforward 3D Reconstruction from Satellite, Drone, and Ground Images](https://mlsanity.com/paper/2605.07978/) (2026)
- [X-Stream: Exploring MLLMs as Multiplexers for Multi-Stream Understanding](https://mlsanity.com/paper/2606.02482/) (2026)
- [OffNadirLoc: Benchmark and Framework for Challenging UAV-to-Satellite Geo-Localization under Large Off-Nadir Views](https://mlsanity.com/paper/2607.19951/) (2026)
- [A Unified Benchmark and Modality-Adaptive Network for Day-and-Night Drone-View Geo-Localization](https://mlsanity.com/paper/2607.25778/) (2026)
- [Calibrating Retrieval Geometry: Reliability-Guided Training-Free Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2609.25937/) (2026)
- [SatNav: A Scalable Benchmark for Long-Horizon UAV Vision-Language Navigation from Satellite Imagery](https://mlsanity.com/paper/2609.31507/) (2026)
- [SatFix: Absolute Visual Localization of UAVs in Satellite Maps from a Single Oblique Image](https://mlsanity.com/paper/2610.11049/) (2026)

## Abstract (arXiv)

The application of unmanned aerial vehicles (UAV) has been widely extended recently. It is crucial to ensure accurate latitude and longitude coordinates for UAVs, especially when the global navigation satellite systems (GNSS) are disrupted and unreliable. Existing visual localization methods achieve autonomous visual localization without error accumulation by matching the ground-down view image of UAV with the ortho satellite maps. However, collecting UAV ground-down view images across diverse locations is costly, leading to a scarcity of large-scale datasets for real-world scenarios. Existing datasets for UAV visual localization are often limited to small geographic areas or are focused only on urban regions with distinct textures. To address this, we define the UAV visual localization task by determining the UAV's real position coordinates on a large-scale satellite map based on the captured ground-down view. In this paper, we present a large-scale dataset, UAV-VisLoc, to facilitate the UAV visual localization task. This dataset comprises images from diverse drones across 11 locations in China, capturing a range of topographical features. The dataset features images from fixed-wing drones and multi-terrain drones, captured at different altitudes and orientations. Our dataset includes 6,742 drone images and 11 satellite maps, with metadata such as latitude, longitude, altitude, and capture date. Our dataset is tailored to support both the training and testing of models by providing a diverse and extensive data.