---
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. 

![Dataset section overview](https://huggingface.co/datasets/fansel/nordland/resolve/main/Nordland%20Partitioned%20Sections.png)

*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}
}
```