| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-to-image |
| tags: |
| - vpr |
| - nordland |
| - seasonal-changes |
| - place-recognition |
| pretty_name: Nordland Dataset (Olid et al. Partition) |
| size_categories: |
| - 10K<n<100K |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: season |
| dtype: |
| class_label: |
| names: |
| '0': fall |
| '1': spring |
| '2': summer |
| '3': winter |
| - name: frame_id |
| dtype: int32 |
| - name: section |
| dtype: |
| class_label: |
| names: |
| '0': train_section1 |
| '1': train_section2 |
| '2': test_section1 |
| '3': test_section2 |
| '4': test_section3 |
| splits: |
| - name: train |
| num_bytes: 161117439484 |
| num_examples: 98280 |
| - name: test |
| num_bytes: 22934069019 |
| num_examples: 13800 |
| download_size: 184051508503 |
| dataset_size: 184051508503 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| - split: test |
| path: data/test-*.parquet |
| --- |
| # Nordland Dataset (Olid et al. Partition) |
| Pre-processed and partitioned version of the Nordland Dataset by Olid et al. |
|
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|  |
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|
| *Figure reproduced from Olid et al. (2018), [Single-View Place Recognition under Seasonal Changes](https://arxiv.org/pdf/1808.06516), Fig. 3. © The original authors.* |
|
|
| ## Dataset Details |
| - **Source video:** [Norwegian Broadcasting Corporation (NRK), *Nordlandsbanen: minute by minute, season by season*](https://nrkbeta.no/2013/01/15/nordlandsbanen-minute-by-minute-season-by-season/ |
| ) |
| - **Pre-processing & partition:** Daniel Olid, Jose M. Facil, Javier Civera, Universidad de Zaragoza |
| - **Paper:** [Single-View Place Recognition under Seasonal Changes](https://arxiv.org/pdf/1808.06516), PPNIV Workshop at IROS 2018 |
| ## Important Things to Know |
| - One frame per second was extracted from the original Nordland videos. Tunnels and train stops were filtered out, leaving 28,865 images per season. |
| - Each image is somewhere between 1m and 25m away from the previous and the next one. |
| - The test split consists of 3 sections of 1,150 images each. |
| - The train split consists of 2 sections, the first with 12,837 images and the second with 11,733. |
| - 200 images were discarded between each section so that train and test sections represent different geographic places. |
| ## Dataset Structure |
| Each sample contains: |
| - `image` — PNG frame extracted from the Nordland train-journey video |
| - `season` — one of `fall`, `spring`, `summer`, `winter` |
| - `frame_id` — global frame index (same index across seasons = same geographic location) |
| - `section` — one of `train_section1`, `train_section2`, `test_section1`, `test_section2`, `test_section3` |
|
|
| ### Sections |
| | Section | `frame_id` range | Images per season | |
| |---------|-----------------|------------------| |
| | `train_section1` | 0 – 12836 | 12,837 | |
| | `train_section2` | 12837 – 24569 | 11,733 | |
| | `test_section1` | 0 – 1149 | 1,150 | |
| | `test_section2` | 1150 – 2299 | 1,150 | |
| | `test_section3` | 2300 – 3449 | 1,150 | |
| ## Usage |
| ```python |
| from datasets import load_dataset, Value |
| |
| ds = load_dataset("fansel/nordland") |
| |
| # season and section are stored as ClassLabel |
| # Cast them to plain strings once for natural filtering: |
| ds = (ds |
| .cast_column("season", Value("string")) |
| .cast_column("section", Value("string"))) |
| |
| # Access splits |
| train, test = ds["train"], ds["test"] |
| |
| # Filter by season |
| winter_test = test.filter(lambda x: x["season"] == "winter") |
| |
| # Filter train section 1, all seasons |
| train_section1 = train.filter(lambda x: x["section"] == "train_section1") |
| |
| # Filter test section 2, season fall |
| test_s2_fall = test.filter( |
| lambda x: x["section"] == "test_section2" and x["season"] == "fall" |
| ) |
| |
| # Cross-season matching: same frame_id across seasons |
| same_location = test.filter(lambda x: x["frame_id"] == 42) |
| for sample in same_location: |
| print(sample["season"], sample["section"]) |
| ``` |
| ## Citation |
| Please cite this publication if you use this partitioned version of the dataset. |
| ```bibtex |
| @article{olidSingleViewPlaceRecognition2018, |
| title = {Single-{View} {Place} {Recognition} under {Seasonal} {Changes}}, |
| volume = {abs/1808.06516}, |
| url = {https://arxiv.org/abs/1808.06516}, |
| journal = {ArXiv preprint}, |
| author = {Olid, Daniel and Fácil, José M. and Civera, Javier}, |
| year = {2018} |
| } |
| ``` |
