Datasets:
Tasks:
Image Segmentation
Sub-tasks:
semantic-segmentation
Languages:
English
Size:
10K<n<100K
License:
| annotations_creators: | |
| - expert-generated | |
| language: | |
| - en | |
| license: cc-by-4.0 | |
| multilinguality: monolingual | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - image-segmentation | |
| task_ids: | |
| - semantic-segmentation | |
| pretty_name: sen1floods11 | |
| dataset_info: | |
| features: | |
| - name: image | |
| dtype: image | |
| - name: label | |
| dtype: mask | |
| splits: | |
| - name: train | |
| - name: validation | |
| - name: test | |
| size_in_MB: 35500 | |
| homepage: https://www.cloudtostreet.info/ | |
| point_of_contact: [email protected] | |
| # Sen1Floods11 | |
| Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1 (Example). This data was generated by Cloud to Street, a Public Benefit Corporation: https://www.cloudtostreet.info/. For questions about this dataset or code please email [email protected]. Please cite this data as: | |
| Bonafilia, D., Tellman, B., Anderson, T., Issenberg, E. 2020. Sen1Floods11: a georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 210-211. | |
| Available Open access at: http://openaccess.thecvf.com/content_CVPRW_2020/html/w11/Bonafilia_Sen1Floods11_A_Georeferenced_Dataset_to_Train_and_Test_Deep_Learning_CVPRW_2020_paper.html | |
| ## Dataset Access | |
| The dataset is available for access through HF at: [senfloods11](https://huggingface.co/datasets/harshinde/sen1floods11/resolve/main/sen1floods11.tar.gz) | |
| ## Bucket Structure | |
| The `sen1floods11` bucket is split into subfolders containing data, checkpoints, training/testing splits, and a [STAC](https://stacspec.org/) compliant catalog. More detail on each is provided in the docs README. | |
| ## Dataset Information | |
| Each file follows the naming scheme EVENT_CHIPID_LAYER.tif (e.g. `Bolivia_103757_S2Hand.tif`). Chip IDs are unique, and not shared between events. Events are named by country and further information on each event (including dates) can be found in the event metadata below. Each layer has a separate GeoTIFF, and can contain multiple bands in a stacked GeoTIFF. All images are projected to WGS 84 (`EPSG:4326`) at 10 m ground resolution. | |
| | Layer | Description | Values | Format | Bands | | |
| | ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------- | ------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | |
| | QC | Hand labeled chips containing ground truth | -1: No Data / Not Valid <br> 0: Not Water <br> 1: Water | GeoTIFF <br> 512 x 512 <br> 1 band <br> Int16 | 0: QC | | |
| | S1 | Raw Sentinel-1 imagery. <br> IW mode, GRD product <br> See [here](https://developers.google.com/earth-engine/sentinel1) for information on preprocessing | Unit: dB | GeoTIFF <br> 512 x 512 <br> 2 bands <br> Float32 | 0: VV <br> 1: VH | | |
| | S2 | Raw Sentinel-2 MSI Level-1C imagery <br> Contains all spectral bands (1 - 12) <br> Does not contain QA mask | Unit: TOA reflectance <br> (scaled by 10000) | GeoTIFF <br> 512 x 512 <br> 13 bands <br> UInt16 | 0: B1 (Coastal) <br> 1: B2 (Blue) <br> 2: B3 (Green) <br> 3: B4 (Red) <br> 4: B5 (RedEdge-1) <br> 5: B6 (RedEdge-2) <br> 6: B7 (RedEdge-3) <br> 7: B8 (NIR) <br> 8: B8A (Narrow NIR) <br> 9: B9 (Water Vapor) <br> 10: B10 (Cirrus) <br> 11: B11 (SWIR-1) <br> 12: B12 (SWIR-2) | | |
| ### Example images | |
| A sample of the dataset for chip _Spain_7370579_ is provided at in `./sample` | |
| <div> | |
| <img src="./docs/img/Spain_7370579_Label.png" height="256" hspace=3 > | |
| <img src="./docs/img/Spain_7370579_S1.png" height="256" hspace=3 > | |
| <img src="./docs/img/Spain_7370579_S2.png" height="256" hspace=3 > | |
| </div> | |
| ## Example Use | |
| [Train.ipynb](Train.ipynb) shows how to train and validate the model on a dataset. | |
| ## Event Metadata | |
| Locations of the flood events and metadata is contained in _Sen1Floods11_Metadata.geojson_. The following fields can be found: | |
| | Field | Description | | |
| | --------------- | -------------------------------------------------------------------------------------- | | |
| | ID | Unique ID for each event | | |
| | location | Flood event location (country) | | |
| | ISO_CC | ISO Country Code for flood event location | | |
| | s1_date | Date (YYYY-MM-dd) that Sentinel-1 image was acquired | | |
| | s2_date | Date (YYYY-MM-dd) that Sentinel-2 image was acquired | | |
| | orbit | Orbit (ASCENDING or DESCENDING) that Sentinel-1 image was acquired | | |
| | rel_orbit_num | Relative Orbit Number that Sentinel-1 image was acquired | | |
| | coincident_size | Number of coincident tiles from S2 | | |
| | VH_thresh | Threshold used for Sentinel-1 VH band to classify water in reference S1 classification | | |
| | train_chip | Number of chips used for training | | |
| | val_chip | Number of chips used for validation | |