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junboboshi
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ETCI-2021-Flood-Detection

Tasks:
Image Segmentation
Modalities:
Geospatial
Languages:
English
Size:
1M<n<10M
Tags:
remote-sensing
earth-observation
geospatial
satellite-imagery
flood-detection
sar-images
License:
Dataset card Files
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request_id: 01KRW8MX4AWR6WRJFTAK2JKESP; (1) not found
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 409, in hf_raise_for_status
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                File "/usr/local/lib/python3.12/site-packages/requests/models.py", line 1026, in raise_for_status
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              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
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                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
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                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
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                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
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                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2240, in __iter__
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                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2159, in _apply_feature_types_on_example
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                                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2204, in decode_example
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                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1508, in decode_nested_example
                  return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 189, in decode_example
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                                   ^^^^^^^^
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                        ^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 1012, in read
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                        ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/hf_file_system.py", line 1078, in read
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                File "/usr/local/lib/python3.12/site-packages/huggingface_hub/utils/_http.py", line 482, in hf_raise_for_status
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ETCI 2021 Flood Detection Dataset

ETCI 2021 Flood Detection

Description

The ETCI 2021 Flood Detection Dataset is a comprehensive flood detection segmentation dataset that focuses on SAR (Synthetic Aperture Radar) images taken by the ESA Sentinel-1 satellite. This dataset provides pairs of VV (Vertical Transmit, Vertical Receive) and VH (Vertical Transmit, Horizontal Receive) polarization images, which have been processed by the Hybrid Pluggable Processing Pipeline (hyp3). Additionally, the dataset includes corresponding binary flood and water body ground truth masks.

The ataset is composed of 66,810 (33,405 x 2 VV & VH polarization) tiles of 256×256 pixels, distributed respectively across the training, validation and test sets as follows: 33,405, 10,400, and 12,348 tiles for each polarization. Each tile includes 3 RGB channels which have been converted by tiling 54 labeled GeoTIFF files generated from Sentinel-1 C-band synthetic aperture radar (SAR) imagery data using Hybrid Pluggable Processing Pipeline “hyp3”. Training tiles correspond to intensity values for VV and VH polarization with the following attributes.

The ETCI 2021 dataset is valuable for flood detection and segmentation tasks and facilitates research and development in this domain.

Details

Structure 

.
├── README.md
└── data
    ├── test
    │   ├── florence_20180510t231343
    │   │   ├── tiles
    │   │   │   ├── flood_label
    │   │   │   │   ├── florence_20180510t231343_x-0_y-0_vv.png
    │   │   │   │   └── ...
    │   │   │   ├── vh
    │   │   │   │   ├── florence_20180510t231343_x-0_y-0_vh.png
    │   │   │   │   └── ...
    │   │   │   ├── vv
    │   │   │   │   ├── florence_20180510t231343_x-0_y-0_vv.png
    │   │   │   │   └── ...
    │   │   │   └── water_body_label
    │   │   │       ├── florence_20180510t231343_x-0_y-0_vv.png
    │   │   │       └── ...
    │   │   └── ...
    │   └── ...
    ├── test_internal
    │   └── ...
    └── train
        └── ...

Statistics

  • Total Number of Images: 66,810
  • SAR Image Resolution: 256x256 pixels
  • Polarization: VV and VH
  • Ground Truth Masks: Binary flood and water body masks
  • Dataset Size: 5.6GB
  • File name prefix: <region>_<datetime>*_x-*_y-*_<vv | vh>.png

Citation

If you use the ETCI 2021 Flood Detection dataset in your research, please consider citing the following publication or the dataset's official website:

https://nasa-impact.github.io/etci2021/
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