MLOps overview#

  • Authors: Sam Budd, Gonzalo Mateo-García


The MLOps section have tutorials for training, testing and running inference of flood extent segmentation models for Sentinel-2. Models are trained in the WorldFloods dataset which is freely accessible. Each of the tutorials is self-contained and can be run on Google Colab.

MLOps diagram

Tutorials on models of Portalés-Julià et al 2023.

Tutorials on models of Mateo-García et al 2021.

  • Train models: trains the WorldFloods model on the WorldFloods V1 dataset from scratch Open In Colab

  • Model metrics: loads a worldfloods trained model and run inference on all images of the WorldFloods V1 test dataset. It computes displays some standard metrics and the PR and ROC curves for water detection. Open In Colab

  • Inference on Sentinel-2 images: loads a worldfloods pretrained model and runs inference on a Sentinel-2 image from the WorldFloods V1 dataset. It shows the predictions vs the ground truth on that image. Open In Colab

Exploratory work:

  • Probabilistic Neural Networks: Run inference of the U-Nets trained with dropout. We apply Bayesian dropout at inference time to obtain an ensemble of predictions. Open In Colab


Mateo-Garcia, G. et al. Towards global flood mapping onboard low cost satellites with machine learning. Scientific Reports 11, 7249 (2021). DOI: 10.1038/s41598-021-86650-z.

Portalés-Julià E., Mateo-García G., Purcell C. and Gómez-Chova L. Global flood extent segmentation in optical satellite images. Scientific Reports 13, 20316 (2023). DOI: 10.1038/s41598-023-47595-7.