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Mapillary Street-level Sequences
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Overview

Mapillary Street-Level Sequences (MSLS) is the largest, most diverse dataset for place recognition, containing 1.6 million images in a large number of short sequences. Spanning 30 cities on six continents, the dataset covers different seasons, weather and daylight conditions, various camera types and viewpoints, diverse architectural and structural settings (such as roadworks), and different levels of dynamic objects present in the scenes (such as moving pedestrians or cars).

Each image comes with metadata and attributes relevant for further research: raw GPS coordinates, capture time, and compass angle, as well as attributes for day/night, and view direction (front-, back-, or side-facing).

We have also run extensive benchmarks on our dataset with previous state-of-the-art methods for place recognition. The results show that training on MSLS improves performance due to the diversity of the dataset in geographical distribution, seasonal and temporal changes, and particularly day/night changes.

Thanks to its wide geographical reach, diversity in scene characteristics, and sufficient size for training neural networks with large capacity, MSLS is the best dataset for pushing the state of the art in visual place recognition and its applications in practical settings across the world.

Features

  • More than 1.6 million images
  • 30 major cities across six continents
  • All images tagged with sequence information, and geo-located with GPS and compass angles
  • Capture times spanning all seasons over a nine-year period
  • Different weather, cameras, daylight conditions, and structural settings

Citation

Please use the following citation when referencing the dataset:

@inproceedings{warburg2020mapillary,
  title={Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition},
  author={Warburg, Frederik and Hauberg, Soren and L{\'o}pez-Antequera, Manuel and Gargallo,
Pau and Kuang, Yubin and Civera, Javier},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={2626--2635},
  year={2020}
}

License

Custom

🎉Many thanks to Graviti Open Datasets for contributing the dataset
Basic Information
Application ScenariosAutonomous DrivingUrban
AnnotationsNo Label
TasksNot Available
LicenseCustom
Updated on2021-03-24 19:48:09
Metadata
Data TypeImageGPS
Data Volume1.6M
Annotation Amount0
File Size0B
Copyright Owner
Mapillary
Annotator
Unknown
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