NOTE: As of v1.1 of Sen1Floods11 the data has been moved to the sen1floods11 bucket on GCS. The original v1.0 data is still hosted for now on the original cnn_chips bucket. v1.1 involved major restructing of the bucket and more understandable file naming as well as updates to the dataset and data format to comply with COG spec.
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 support@cloudtostreet.info. 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
The dataset is available for access through Google Cloud Storage bucket at: gs://senfloods11/
You can access the dataset bucket using the gsutil command line tool. If you would like to download the entire dataset (~14 GB) you can use gsutil rsync to clone the bucket to a local directory. The -m flag is recommended to speed downloads. The -r flag will download sub-directories and folder recursively. See the example below.
$ gsutil -m rsync -r gs://sen1floods11 /YOUR/LOCAL/DIRECTORY/HEREIf using an example notebook, you can download the dataset to the folder that notebooks expect it to be in by running
$ mkdir /home/files
$ gsutil -m rsync -r gs://sen1floods11 /home/filesThe 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.
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) |
A sample of the dataset for chip Spain_7370579 is provided at in ./sample
Train.ipynb shows how to train and validate the model on a dataset.
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 |