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Datasets:
adiui
/
FAIR1M

Tasks:
Object Detection
Modalities:
Geospatial
Languages:
English
ArXiv:
Tags:
remote-sensing
earth-observation
geospatial
satellite-imagery
object-detection
License:
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    FileNotFoundError
Message:      [Errno 2] No such file or directory: '<datasets.utils.file_utils.FilesIterable object at 0x7f2d2ed16ab0>'
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4379, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2661, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2839, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/xml/xml.py", line 67, in _generate_tables
                  with open(file, encoding=self.config.encoding, errors=self.config.encoding_errors) as f:
                       ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/streaming.py", line 73, in wrapper
                  return function(*args, download_config=download_config, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 967, in xopen
                  return open(main_hop, mode, *args, **kwargs)
              FileNotFoundError: [Errno 2] No such file or directory: '<datasets.utils.file_utils.FilesIterable object at 0x7f2d2ed16ab0>'

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FAIR1M

FAIR1M

The FAIR1M dataset is a fine-grained object recognition and detection dataset that focuses on high-resolution (0.3-0.8m) RGB images taken by the Gaogen (GF) satellites and extracted from Google Earth. It consists of a collection of 15,000 high-resolution images that cover various objects and scenes. The dataset provides annotations in the form of rotated bounding boxes for objects belonging to 5 main categories (ships, vehicles, airplanes, courts, and roads), further divided into 37 sub-categories.

Description

FAIR1M is a part of the ISPRS Benchmark on Object Detection in High-Resolution Satellite Images. Please note that, as of now, only a portion of the training dataset (1,732/15,000 images) has been released for the challenge.

  • 1 million object instances
  • Number of Samples: 15000
  • Bands: 3 (RGB)
  • Image Size: 1024x1024
  • Image Resolution: 0.3–0.8m
  • Land Cover Classes: 37
  • Classes: 5 object categories, 37 object sub-categories.
  • Scene Categories: Passenger Ship, Motorboat, Fishing Boat, Tugboat, other-ship, Engineering Ship, Liquid Cargo Ship, Dry Cargo Ship, Warship, Small Car, Bus, Cargo Truck, Dump Truck, other-vehicle, Van, Trailer, Tractor, Excavator, Truck Tractor, Boeing737, Boeing747, Boeing777, Boeing787, ARJ21, C919, A220, A321, A330, A350, other-airplane, Baseball Field, Basketball Court, Football Field, Tennis Court, Roundabout, Intersection, Bridge
  • Source: Gaofen/Google Earth

Usage

To use this dataset, simply use datasets.load_dataset("blanchon/FAIR1M").

from datasets import load_dataset
FAIR1M = load_dataset("blanchon/FAIR1M")

Citation

If you use the FAIR1M dataset in your research, please consider citing the following publication:

@article{sun2021fair1m,
    title     = {FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery},
    author    = {Xian Sun and Peijin Wang and Zhiyuan Yan and F. Xu and Ruiping Wang and W. Diao and Jin Chen and Jihao Li and Yingchao Feng and Tao Xu and M. Weinmann and S. Hinz and Cheng Wang and K. Fu},
    journal   = {Isprs Journal of Photogrammetry and Remote Sensing},
    year      = {2021},
    doi       = {10.1016/j.isprsjprs.2021.12.004},
    bibSource = {Semantic Scholar https://www.semanticscholar.org/paper/6d3c2dc63ff0deec10f60e5a515c93af4f8676f2}
}
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