fansel/freiburg-across-seasons

Freiburg Across Seasons Dataset

Image sequences recorded in the city of Freiburg across three years for benchmarking long-term, season-invariant Visual Place Recognition (VPR). The recordings were captured by Naseer, Spinello, Stachniss and Burgard at the University of Freiburg with a forward-facing Bumblebee stereo camera mounted on a car. The dataset preparation, ground-truth annotation and original distribution are by the authors. This Hugging Face dataset card repackages the data for convenient consumption via the datasets library; the underlying recordings, processing, and ground truth are theirs.

Dataset Details

• Source recordings & ground truth: Tayyab Naseer, Luciano Spinello, Cyrill Stachniss, Wolfram Burgard, University of Freiburg
• Papers:
  • Naseer, Burgard, Stachniss. Robust Visual Localization Across Seasons. IEEE Transactions on Robotics (TRO), 2018.
  • Naseer, Spinello, Burgard, Stachniss. Robust Visual Robot Localization Across Seasons using Network Flows. AAAI, 2014.
• Original distribution: http://aisdatasets.informatik.uni-freiburg.de/freiburg_across_seasons/
• License: CC BY-SA 4.0 (per the authors' original release)

Recording Setup

The dataset preparation follows Naseer et al. (2014/2018):

• Recordings were captured with a forward-facing Bumblebee stereo camera mounted on a car. In summer the camera was mounted outside the car, in winter inside.
• All images are 1024×768 (width×height), JPEG-compressed.
• No preprocessing was applied to the recorded images.
• All images are geo-tagged with corresponding GPS positions (recorded with an inexpensive GPS sensor).
• Three sequences were recorded:
  • Localization-1 (Summer 2012): 6,915 images at 1 Hz, covering 10 km.
  • Mapping (Winter 2012): 30,790 images at 4 Hz, covering 50 km.
  • Localization-2 (Summer 2015): 5,392 images.
• Ground truth was created by hand-labelling matches after manual visual inspection. Localization-1 has 3,656 ground-truth-matched images to the Mapping sequence, Localization-2 has 4,477. In total, 8,133 ground-truth matchings are provided.

What this Hugging Face version contains

This Hugging Face card distributes a subset of the original Freiburg Across Seasons release:

• Camera 0 only. Only the left camera of the Bumblebee stereo pair is included. The right-camera stream is not part of this distribution, so stereo depth extraction is not possible from this version. For the full stereo data, refer to the original release.
• Ground-truth matchings only as annotations. The manually annotated query→reference correspondences from Naseer et al. are provided as Parquet files in matchings/. No additional labels (semantic, depth, pose) are included.

Traversals

Folder	Sequence	Season	Images
summer_icra_dataset/	Localization-1	Summer 2012	6,915
summer2015_icra_dataset/	Localization-2	Summer 2015	5,392
winter_icra_dataset/	Mapping	Winter 2012	30,790

Ground-truth matchings

Located in matchings/:

File	Query traversal	Reference traversal	Pairs
summer2012_winter_matchings.parquet	summer2012	winter	4,037
summer2015_winter_matchings.parquet	summer2015	winter	4,739

Each Parquet file has two integer columns, <season>_idx and winter_idx, which are zero-based indices into the corresponding image folder sorted lexicographically by filename.

Loading example

import pandas as pd
from PIL import Image
import os

df = pd.read_parquet("matchings/summer2015_winter_matchings.parquet")
summer_imgs = sorted(os.listdir("summer2015_icra_dataset/"))
winter_imgs = sorted(os.listdir("winter_icra_dataset/"))

query = Image.open(f"summer2015_icra_dataset/{summer_imgs[df.iloc[0]['summer2015_idx']]}")
match = Image.open(f"winter_icra_dataset/{winter_imgs[df.iloc[0]['winter_idx']]}")

License

Released under CC BY-SA 4.0, matching the original distribution by Naseer et al. Attribution and ShareAlike conditions apply.

Citation

Please cite these publications if you use this dataset. The papers can be found here: Robust Visual Localization Across Seasons and Robust Visual Robot Localization Across Seasons using Network Flows.

@article{naseerRobustVisualLocalization2018,
    title   = {Robust Visual Localization Across Seasons},
    author  = {Naseer, Tayyab and Burgard, Wolfram and Stachniss, Cyrill},
    journal = {IEEE Transactions on Robotics},
    year    = {2018}
}

@inproceedings{naseerRobustVisualRobot2014,
    title     = {Robust Visual Robot Localization Across Seasons using Network Flows},
    author    = {Naseer, Tayyab and Spinello, Luciano and Burgard, Wolfram and Stachniss, Cyrill},
    booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
    address   = {Quebec, Canada},
    year      = {2014}
}