The WorldFloods database

The WorldFloods database contains 424 pairs of Sentinel-2 images and flood segmentation masks. It requires approximately 300GB of hard-disk storage. The WorldFloods database is released under a Creative Commons non-commercial licence

Download the data from the Google Drive

A subset of the data and the pretrained models are available in this public Google Drive folder.

If you want to use this data from the Google Colab you can ‘add a shortcut to your Google Drive’ from the public Google Drive folder and mount that directory:

from google.colab import drive
drive.mount('/content/drive')
!ls '/content/drive/My Drive/Public WorldFloods Dataset'

Alternatively you can download it manually from that folder or even automatically with the gdown package.

gdown --id 11O6aKZk4R6DERIx32o4mMTJ5dtzRRKgV

Download the data from the Google Bucket

The database is available in this Google bucket: gs://ml4cc_data_lake/2_PROD/2_Mart/worldfloods_v1_0/. This Google bucket is in requester pays mode, hence you’d need a GCP project to download the data. To download the entire dataset run:

gsutil -u your-project cp gs://ml4cc_data_lake/2_PROD/2_Mart/worldfloods_v1_0/worldfloods_v1_0.zip .

If you want only an specific subset (train, train_sample, val or test) run:

gsutil -u your-project cp gs://ml4cc_data_lake/2_PROD/2_Mart/worldfloods_v1_0/train_v1_0.zip .
gsutil -u your-project cp gs://ml4cc_data_lake/2_PROD/2_Mart/worldfloods_v1_0/train_sample_v1_0.zip .
gsutil -u your-project cp gs://ml4cc_data_lake/2_PROD/2_Mart/worldfloods_v1_0/val_v1_0.zip .
gsutil -u your-project cp gs://ml4cc_data_lake/2_PROD/2_Mart/worldfloods_v1_0/test_v1_0.zip .

If you want to download the pre-trained models of this work run:

mkdir WFV1_scnn20
gsutil -u your-project cp gs://ml4cc_data_lake/2_PROD/2_Mart/2_MLModelMart/WFV1_scnn20/config.json WFV1_scnn20/
gsutil -u your-project cp gs://ml4cc_data_lake/2_PROD/2_Mart/2_MLModelMart/WFV1_scnn20/model.pt WFV1_scnn20/

mkdir WFV1_unet
gsutil -u your-project cp gs://ml4cc_data_lake/2_PROD/2_Mart/2_MLModelMart/WFV1_unet/config.json WFV1_unet/
gsutil -u your-project cp gs://ml4cc_data_lake/2_PROD/2_Mart/2_MLModelMart/WFV1_unet/model.pt WFV1_unet/

If you find this work useful please cite:

@article{mateo-garcia_towards_2021,
	title = {Towards global flood mapping onboard low cost satellites with machine learning},
	volume = {11},
	issn = {2045-2322},
	doi = {10.1038/s41598-021-86650-z},
	number = {1},
	urldate = {2021-04-01},
	journal = {Scientific Reports},
	author = {Mateo-Garcia, Gonzalo and Veitch-Michaelis, Joshua and Smith, Lewis and Oprea, Silviu Vlad and Schumann, Guy and Gal, Yarin and Baydin, Atılım Güneş and Backes, Dietmar},
	month = mar,
	year = {2021},
	pages = {7249},
}