Datasets:
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
Bucket Structure
The sen1floods11 bucket is split into subfolders containing data, checkpoints, training/testing splits, and a STAC 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 0: Not Water 1: Water |
GeoTIFF 512 x 512 1 band Int16 |
0: QC |
| S1 | Raw Sentinel-1 imagery. IW mode, GRD product See here for information on preprocessing |
Unit: dB | GeoTIFF 512 x 512 2 bands Float32 |
0: VV 1: VH |
| S2 | Raw Sentinel-2 MSI Level-1C imagery Contains all spectral bands (1 - 12) Does not contain QA mask |
Unit: TOA reflectance (scaled by 10000) |
GeoTIFF 512 x 512 13 bands UInt16 |
0: B1 (Coastal) 1: B2 (Blue) 2: B3 (Green) 3: B4 (Red) 4: B5 (RedEdge-1) 5: B6 (RedEdge-2) 6: B7 (RedEdge-3) 7: B8 (NIR) 8: B8A (Narrow NIR) 9: B9 (Water Vapor) 10: B10 (Cirrus) 11: B11 (SWIR-1) 12: B12 (SWIR-2) |
Example images
A sample of the dataset for chip Spain_7370579 is provided at in ./sample
Example Use
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 |