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Error code: DatasetGenerationError Exception: UnicodeDecodeError Message: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single for _, table in generator: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/text/text.py", line 90, in _generate_tables batch = f.read(self.config.chunksize) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 1104, in read_with_retries out = read(*args, **kwargs) File "/usr/local/lib/python3.9/codecs.py", line 322, in decode (result, consumed) = self._buffer_decode(data, self.errors, final) UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1529, in compute_config_parquet_and_info_response parquet_operations = convert_to_parquet(builder) File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet builder.download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare self._download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
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This very-high-resolution (VHR) remote sensing image dataset was constructed by Dr. Gong Cheng et al. from Northwestern Polytechnical University (NWPU). |
This is a 10-class geospatial object detection dataset used for research purposes only.These ten classes of objects are airplane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, and vehicle. |
This dataset contains totally 800 VHR remote sensing images, where the folder "negative image set" includes 150 images that do not contain any targets of the given object classes and the folder "positive image set" includes 650 images with each image containing at least one target to be detected. |
These images were cropped from Google Earth and Vaihingen data set and then manually annotated by experts. The Vaihingen data was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF): http://www.ifp.uni-stuttgart.de/dgpf/DKEPAllg.html. |
The folder "ground truth" contains 650 separate text files and each one corresponds to an image in "positive image set" folder. Each line of those text files defines a ground truth bounding box in the following format: |
(x1,y1),(x2,y2),a |
where (x1,y1) denotes the top-left coordinate of the bounding box, (x2,y2) denotes the right-bottom coordinate of the bounding box, and a is the object class (1-airplane, 2-ship, 3-storage tank, 4-baseball diamond, 5-tennis court, 6-basketball court, 7-ground track field, 8-harbor, 9-bridge, 10-vehicle). |
Please cite the following relevant papers when publishing results that use this dataset fully or partly: |
Gong Cheng, Junwei Han, Peicheng Zhou, Lei Guo. Multi-class geospatial object detection and geographic image classification based on collection of part detectors. ISPRS Journal of Photogrammetry and Remote Sensing, 98: 119-132, 2014. |
Gong Cheng, Junwei Han. A survey on object detection in optical remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 117: 11-28, 2016. |
Gong Cheng, Peicheng Zhou, Junwei Han. Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 54(12): 7405-7415, 2016. |
Contact: [email protected];[email protected] |
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End of preview.
Redistributed without modification from https://drive.google.com/file/d/1--foZ3dV5OCsqXQXT84UeKtrAqc5CkAE and https://github.com/chaozhong2010/VHR-10_dataset_coco. Converted the RAR file to a ZIP file.
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