Pith. sign in

REVIEW 8 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2405.11936 v1 pith:LTUWXUCD submitted 2024-05-20 cs.CV

Wenjia Xu , Yaxuan Yao , Jiaqi Cao , Zhiwei Wei , Chunbo Liu , Jiuniu Wang , Mugen Peng This is my paper
classification cs.CV
keywords datasetlocalizationvisualimageslarge-scalesatellitediversedrones
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Visual Place Recognition for Large-Scale UAV Applications

    cs.CV 2025-07 conditional novelty 7.0 of 10

    A million-image aerial place recognition dataset from Estonia, plus a demonstration that steerable (rotation-equivariant) CNNs give robust gains over standard ResNet baselines in aerial visual place recognition.

  2. A Unified Benchmark and Modality-Adaptive Network for Day-and-Night Drone-View Geo-Localization

    cs.CV 2026-07 conditional novelty 6.0 of 10

    IRCHN is the first drone-view geo-localization benchmark with geographically aligned visible, infrared, and satellite images, and MASTR-Net improves retrieval across both modalities.

  3. RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs

    cs.CV 2026-07 conditional novelty 6.0 of 10

    One frozen DINOv2 token field, shared between a frozen retrieval head and a distilled local-descriptor decoder, performs UAV 6-DoF localization 1.8× faster than separate encoders with re-ranking within ~1 pp.

  4. UAVScenes: A Multi-Modal Dataset for UAVs

    cs.CV 2025-07 conditional novelty 6.0 of 10

    UAVScenes adds frame-wise image and LiDAR semantic labels, reconstructed 6-DoF poses, and 3D maps to 120k frames of the MARS-LVIG dataset, with six benchmark tasks.

  5. Learning Dense Feature Matching via Lifting Single 2D Image to 3D Space

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A dense feature matching model trained on synthetic multi-view data lifted from single images outperforms prior methods on zero-shot pose estimation benchmarks.

  6. OffNadirLoc: Benchmark and Framework for Challenging UAV-to-Satellite Geo-Localization under Large Off-Nadir Views

    cs.CV 2026-07 conditional novelty 5.0 of 10

    A new benchmark and framework for matching sharply angled UAV photos to satellite maps, with an aggregation and group-learning method that outperforms prior work on most tested datasets.

  7. AeroMap3D: Anchoring Monocular UAV 6-DoF Localization to Visual-Geometric-Semantic Map Priors

    cs.RO 2026-07 conditional novelty 5.0 of 10

    AeroMap3D combines a scale/yaw adapter, a frozen dense matcher, OSM-filtered DEM lifting, and an EKF to achieve 5.88 m mean 3D localization error over 55 km of UAV flight using only public maps.

  8. Hierarchical Image Matching for UAV Absolute Visual Localization via Semantic and Structural Constraints

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A hierarchical matcher combining DINOv2 semantic features with lightweight keypoint matching improves UAV-to-satellite localization success rate from about 0.5 to over 0.8 on the AerialVL and new CS-UAV benchmarks.

Pith tools