Datasets:
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---
license: cc-by-nc-sa-4.0
task_categories:
- feature-extraction
language:
- en
size_categories:
- 100K<n<1M
---
## Nordland Dataset
This dataset is from the original videos released here: [https://nrkbeta.no/2013/01/15/nordlandsbanen-minute-by-minute-season-by-season/](https://nrkbeta.no/2013/01/15/nordlandsbanen-minute-by-minute-season-by-season/)
### Citation Information
Please cite the original publication if you use this dataset.
Sünderhauf, Niko, Peer Neubert, and Peter Protzel. "Are we there yet? Challenging SeqSLAM on a 3000 km journey across all four seasons." Proc. of Workshop on Long-Term Autonomy, IEEE International Conference on Robotics and Automation (ICRA). 2013.
```bibtex
@inproceedings{sunderhauf2013we,
title={Are we there yet? Challenging SeqSLAM on a 3000 km journey across all four seasons},
author={S{\"u}nderhauf, Niko and Neubert, Peer and Protzel, Peter},
booktitle={Proc. of workshop on long-term autonomy, IEEE international conference on robotics and automation (ICRA)},
pages={2013},
year={2013}
}
```
### Dataset Description
The Nordland dataset captures a 728 km railway journey in Norway across four seasons: spring, summer, fall, and winter.
It is organised into four folders, each named after a season and containing 35,768 images.
These images maintain a one-to-one correspondence across folders.
For each traverse, the corresponding ground truth data is available in designated .csv files.
We have also included a file named `nordland_imageNames.txt`, which offers a filtered list of images.
This selection excludes segments captured when the train's speed fell below 15 km/h, as determined by the accompanying GPS data.
### Our Utilisation
We have used this dataset for the three publications below:
* Ensembles of Modular SNNs with/without sequence matching: [Applications of Spiking Neural Networks in Visual Place Recognition](https://arxiv.org/abs/2311.13186)
* Modular SNN: [Ensembles of Compact, Region-specific & Regularized Spiking Neural Networks for Scalable Place Recognition (ICRA 2023)](https://arxiv.org/abs/2209.08723) DOI: [10.1109/ICRA48891.2023.10160749](https://doi.org/10.1109/ICRA48891.2023.10160749)
* Non-modular SNN: [Spiking Neural Networks for Visual Place Recognition via Weighted Neuronal Assignments (RAL + ICRA2022)](https://arxiv.org/abs/2109.06452) DOI: [10.1109/LRA.2022.3149030](https://doi.org/10.1109/LRA.2022.3149030)
The code for our three papers mentioned above is publicly available at: [https://github.com/QVPR/VPRSNN](https://github.com/QVPR/VPRSNN) |