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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/text/text.py", line 98, in _generate_tables
                  batch = f.read(self.config.chunksize)
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
                  out = read(*args, **kwargs)
                File "<frozen codecs>", line 325, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x89 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 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, 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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UAVScenes

(ICCV 2025) UAVScenes: A Multi-Modal Dataset for UAVs

[arXiv] [ICCV 2025]

We introduce UAVScenes, a large-scale dataset designed to benchmark various tasks across both 2D and 3D modalities. Our benchmark dataset is built upon the well-calibrated multi-modal UAV dataset MARS-LVIG, originally developed only for simultaneous localization and mapping (SLAM). We enhance this dataset by providing manually labeled semantic annotations for both images and LiDAR point clouds, along with accurate 6-degree-of-freedom (6-DoF) poses. These additions enable a wide range of UAV perception tasks, including detection, segmentation, depth estimation, 6-DoF localization, place recognition, and novel view synthesis (NVS). To the best of our knowledge, this is the first UAV benchmark dataset to offer both image and LiDAR point cloud semantic annotations (120k labeled pairs), with the potential to advance multi-modal UAV perception research.

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Download

We provide both the full dataset (interval=1) and the key-frame only dataset (interval=5, 1/5 size).
UAVScenes has been uploaded onto various cloud platforms.

We currently include:

  • Hikvision camera images with annotations
  • Livox Avia LiDAR point clouds with annotations
  • 6-DoF poses
  • Reconstructed 3D point cloud/mesh maps

File Information

Color mapping is in cmap.py.
Camera-LiDAR calibrations are in calibration_results.py.
Camera-3D map calibrations are in sampleinfos_interpolated.json.

terra_ply/ contains the raw mesh map outputs from Terra, which contains multiple mesh blocks.
cloud_merged.ply contains the raw point cloud map outputs from Terra.
Mesh.ply is built by merging all mesh blocks from terra_ply/ together.

Dataset Overview

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  • UAVScenes consists of 4 large scenes (AMtown, AMvalley, HKairport, and HKisland). Each scene consists of multiple runs (e.g., 01, 02, and 03).
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Baseline Code

Under preparing. Please stay tuned.

Citation 

@article{wang2025uavscenes,
  title={UAVScenes: A Multi-Modal Dataset for UAVs},
  author={Wang, Sijie and Li, Siqi and Zhang, Yawei and Yu, Shangshu and Yuan, Shenghai and She, Rui and Guo, Quanjiang and Zheng, JinXuan and Howe, Ong Kang and Chandra, Leonrich and others},
  journal={arXiv preprint arXiv:2507.22412},
  year={2025}
}

License

This work is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and is meant for academic use only.

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