Datasets:

ckyrkou
/

disaster36k_fold4

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label disaster36k_fold4@e9ad5875ea072f9e897949d5c58f27d63b213bce
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2386, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2303, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2178, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1483, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1158, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1095, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1116, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label disaster36k_fold4@e9ad5875ea072f9e897949d5c58f27d63b213bce

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UAVDisaster36K Dataset

Overview

UAVDisaster36K is a large-scale aerial image dataset for disaster recognition collected from UAVs, drones, and other low-altitude aerial platforms. The dataset contains images of disaster and non-disaster scenarios across four categories:

Earthquake
Flood
Fire
Normal

The dataset was created to support research in aerial image classification, disaster monitoring, emergency response, and efficient deep learning models for UAV deployment.

Repository Contents

Zip file containint the 4th fold files which demonstrated the most correlration with the average.

Dataset Statistics

Class	Images	Videos
Earthquake	6,595	45
Flood	8,905	37
Fire	10,160	45
Normal	10,340	32
Total	36,000	159

Data Collection

Images were collected from:

UAVs (Unmanned Aerial Vehicles)
Drones
Low-altitude aircraft

Data acquisition was performed using disaster-related keywords targeting aerial imagery of:

Earthquakes
Floods
Wildfires
Normal non-disaster scenes

Data Cleaning and Validation

The dataset was manually reviewed and curated by two researchers.

The cleaning process involved:

Removal of irrelevant images
Removal of branded and copyrighted content
Removal of terrestrial (non-aerial) imagery
Removal of blurry and low-quality samples
Verification of class labels

Data Partitioning

To minimize data leakage, the dataset was partitioned using a video-based splitting strategy, ensuring that frames originating from the same video do not appear in multiple splits.

The official experiments use:

Training set
Validation set
Test set

Acquisition Characteristics

Altitude

Approximate acquisition altitude:

10 m to 300 m

Viewing Angles

Images range from:

Near-nadir views
Oblique aerial views

Data Format

Images: .jpg

Image Resolution Statistics

Earthquake

Width: Min 694, Max 1920, Mean 1310.20
Height: Min 480, Max 1080, Mean 763.71

Flood

Width: Min 960, Max 1920, Mean 1329.70
Height: Min 620, Max 1080, Mean 749.72

Fire

Width: Min 640, Max 1920, Mean 1200.82
Height: Min 360, Max 1080, Mean 680.11

Normal

Width: Min 426, Max 1920, Mean 1103.36
Height: Min 240, Max 1080, Mean 597.08

Applications

Disaster Recognition
Aerial Image Classification
UAV-Based Emergency Response
Vision Transformers
Edge AI for UAVs
Computer Vision Research

Citation

@article{shianios2026uavdisaster36k,
  title={Advancing Efficiency and Accuracy in Aerial Image Classification for Disaster Incidents with SqueezeViT and UAVDisaster36K benchmark},
  author={Shianios, Demetris and Kyrkou, Christos},
  journal={Neurocomputing},
  volume={681},
  pages={133282},
  year={2026},
  publisher={Elsevier}
}

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