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Mapillary Street-level Sequences
No Label
Urban
|Autonomous Driving
|...
License: Unknown

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}
}
Data Summary
Type
GPS, Image,
Amount
1600K
Size
54143.07GB
Provided by
Mapillary
Mapillary is the platform that makes street-level images and map data available to scale and automate mapping. We're committed to building a global service for everyone.
| Amount 1600K | Size 54143.07GB
Mapillary Street-level Sequences
No Label
Urban | Autonomous Driving
License: Unknown

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}
}
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