Nordland Dataset (Olid et al. Partition)

Pre-processed and partitioned version of the Nordland Dataset by Olid et al.

Figure reproduced from Olid et al. (2018), Single-View Place Recognition under Seasonal Changes, Fig. 3. © The original authors.

Dataset Details

• Source video: Norwegian Broadcasting Corporation (NRK), 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, 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

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.

@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}
}