OpenCVL

OpenCVL: An Open, Diverse, and Large-Scale Dataset for Fine-Grained Cross-View Localization

ECCV 2026

Zimin Xia1 2 * †, Mubariz Zaffar3 *, Junsheng Fu4, Alexandre Alahi1, and Julian F. P. Kooij3

1 École Polytechnique Fédérale de Lausanne (EPFL), Switzerland 2 Southern University of Science and Technology (SUSTech), China 3 Delft University of Technology, The Netherlands 4 Zenseact *Equal contribution, †corresponding author

Download Samples Access Dataset Developer Tools Paper
4
countries
41
cities
7,000+ km2
coverage
617,388
ground-aerial pairs

Open, diverse, and large-scale data for fine-grained CVL.

Open

Ground imagery comes from ZOD and Mapillary, paired with aerial imagery from national mapping agencies with open access.

Diverse

The dataset combines calibrated vehicle-mounted sensors with in-the-wild images from pedestrians, cyclists, cars, and other capture platforms.

Large-scale

OpenCVL covers four countries, 41 cities, more than 7,000 km2, and 617,388 ground-aerial image pairs.

Country-level data statistics and aerial resolution.

OpenCVL spans Sweden, the Netherlands, Poland, and Norway. Each ground-level image is paired with a 100 m by 100 m aerial crop from national open mapping sources.

OpenCVL coverage map across Sweden, Norway, the Netherlands, and Poland with sample ground and aerial images.
Sweden 327,647 ground-aerial pairs
ZOD
241,107
Mapillary
86,540
Resolution
0.16 m/pixel
Netherlands 93,062 ground-aerial pairs
ZOD
2,314
Mapillary
90,748
Resolution
0.045 m/pixel
Poland 147,173 ground-aerial pairs
ZOD
29,663
Mapillary
117,510
Resolution
0.1 m/pixel
Norway 49,506 ground-aerial pairs
ZOD
1,403
Mapillary
48,103
Resolution
0.04-0.1 m/pixel

Reliable supervision plus real-world diversity.

ZOD ground imagery

Vehicle-mounted front-facing cameras, LiDAR, and high-end GNSS provide accurate pose supervision for training and curated evaluation.

274,487 images

Mapillary imagery

Crowd-sourced street-level imagery adds broad variation in camera type, mounting, viewpoint, time, weather, and scene content.

342,901 images

Open aerial sources

Aerial crops are retrieved from national open-data mapping sources for Sweden, the Netherlands, Poland, and Norway.

0.04-0.16 m/pixel
Diverse OpenCVL examples with ground images above their corresponding aerial views.
Diverse ground-level captures paired with aerial views: pedestrian, cyclist, snowy, urban, night, and road scenes.

Splits for scale, generalization, seasons, and in-the-wild robustness.

Training split

Large-scale training

Combines ZOD and Mapillary ground imagery to scale supervised learning with both accurate pose supervision and broad viewpoint diversity.

Primary goal
Large-scale training
Ground source
ZOD + Mapillary

Dataset release and project resources.

Download Samples Preview a small OpenCVL sample package.
Access Dataset Register, then continue to the full dataset archives.
Development Tools Code and tooling will be released soon.
Paper Paper link and citation details for ECCV 2026.

Citation

@inproceedings{opencvl2026,
  title = {OpenCVL: An Open, Diverse, and Large-Scale Dataset for Fine-Grained Cross-View Localization},
  author = {Xia, Zimin and Zaffar, Mubariz and Fu, Junsheng and Alahi, Alexandre and Kooij, Julian F. P.},
  booktitle = {European Conference on Computer Vision},
  year = {2026}
}