Global Flood Monitoring (GFM) new release v2.0.0

The Copernicus Emergency Management Service has been developing a new operational product providing a continuous global, systematic, and automated monitoring of all land surface areas possibly affected by flooding. This new global flood monitoring (GFM) product processes all incoming Sentinel-1 images and analyses them using an ensemble of 3 flood detection algorithms providing a high timeliness and quality of the product. The workshop, in the form of a webinar, will present the currently available data and product that have been developed as part of the GFM focusing on the high-resolution satellite-based products for flood monitoring at global scale, freely accessible in real-time through GloFAS

Global Flood Monitoring Webinar 2022: Products Outline

The Copernicus Emergency Management Service has been developing a new operational product providing a continuous global, systematic, and automated monitoring of all land surface areas possibly affected by flooding. This new global flood monitoring (GFM) product processes all incoming Sentinel-1 images and analyses them using an ensemble of 3 flood detection algorithms providing a high timeliness and quality of the product. The workshop, in the form of a webinar, will present the currently available data and product that have been developed as part of the GFM focusing on the high-resolution satellite-based products for flood monitoring at global scale, freely accessible in real-time through GloFAS

Sentinel-1-based water and flood mapping: benchmarking convolutional neural networks against an operational rule-based processing chain

In this study, the effectiveness of several convolutional neural network architectures (AlbuNet-34/FCN/DeepLabV3+/U-Net/U-Net++) for water and flood mapping using Sentinel-1 amplitude data is compared to an operational rule-based processor (S-1FS). This comparison is made using a globally distributed dataset of Sentinel-1 scenes and the corresponding ground truth water masks derived from Sentinel-2 data to evaluate the performance of the classifiers on a global scale in various environmental conditions. The impact of using single versus dual-polarized input data on the segmentation capabilities of AlbuNet-34 is evaluated. The weighted cross entropy loss is combined with the Lovász loss and various data augmentation methods are investigated. Furthermore, the concept of atrous spatial pyramid pooling used in DeepLabV3+ and the multiscale feature fusion inherent in U-Net++ are assessed. Finally, the generalization capacity of AlbuNet-34 is tested in a realistic flood mapping scenario by using additional data from two flood events and the Sen1Floods11 dataset. The model trained using dual polarized data outperforms the S-1FS significantly and increases the intersection over union (IoU) score by 5%. Using a weighted combination of the cross entropy and the Lovász loss increases the IoU score by another 2%. Geometric data augmentation degrades the performance while radiometric data augmentation leads to better testing results. FCN/DeepLabV3+/U-Net/U-Net++ perform not significantly different to AlbuNet-34. Models trained on data showing no distinct inundation perform very well in mapping the water extent during two flood events, reaching IoU scores of 0.96 and 0.94, respectively, and perform comparatively well on the Sen1Floods11 dataset

S1S2-Water: A global dataset for semantic segmentation of water bodies from Sentinel-1 and Sentinel-2 satellite images Wieland, Fichtner, Martinis, Groth, Krullikowski, Plank, Motagh

This study introduces the S1S2-Water dataset - a global reference dataset for training, validation and testing of convolutional neural networks for semantic segmentation of surface water bodies in publicly available Sentinel-1 and Sentinel-2 satellite images. The dataset consists of 65 triplets of Sentinel-1 and Sentinel-2 images with quality checked binary water mask. Samples are drawn globally on the basis of the Sentinel-2 tile-grid (100 x 100 km) under consideration of pre-dominant landcover and availability of water bodies. Each sample is complemented with metadata and Digital Elevation Model (DEM) raster from the Copernicus DEM. On the basis of this dataset we carry out performance evaluation of convolutional neural network architectures to segment surface water bodies from Sentinel-1 and Sentinel-2 images. We specifically evaluate the influence of image bands, elevation features (slope) and data augmentation on the segmentation performance and identify best-performing baseline-models. The model for Sentinel-1 achieves an Intersection Over Union of 0.845, Precision of 0.932 and Recall of 0.896 on the test data. For Sentinel-2 the best model produces an Intersection Over Union of 0.965, Precision of 0.989 and Recall of 0.951 respectively. We also evaluate the performance impact when a model is trained on permanent water data and applied to independent test scenes of floods. The S1S2-Water dataset is released openly and available for download: https://doi.org/10.5281/zenodo.8314175

A likelihood analysis of the Global Flood Monitoring ensemble product

Flooding is a natural disaster that can have devastating impacts on communities and individuals, causing significant damage to infrastructure, loss of life, and economic disruption. The Global Flood Monitoring (GFM) system of the Copernicus Emergency Management Service (CEMS) addresses these challenges and provides global, near-real time flood extent masks for each newly acquired Sentinel-1 Interferometric Wide Swath Synthetic Aperture Radar (SAR) image, as well as archive data from 2015 on, and therefore supports decision makers and disaster relief actions. The GFM flood extent is an ensemble product based on a combination of three independently developed flood mapping algorithms that individually derive the flood information from Sentinel-1 data. Each flood algorithm also provides classification uncertainty information as flood classification likelihood that is aggregated in the same ensemble process. All three algorithms utilize different methods both for flood detection and the derivation of uncertainty information. The first algorithm applies a threshold-based flood detection approach and provides uncertainty information through fuzzy memberships. The second algorithm applies a change detection approach where the classification uncertainty is expressed through classification probabilities. The third algorithm applies the Bayes decision theorem and derives uncertainty information through the posterior probability of the less probable class. The final GFM ensemble likelihood layer is computed with the mean likelihood on pixel level. As the flood detection algorithms derive uncertainty information with different methods, the value range of the three input likelihoods must be harmonized to a range from low [0] to high [100] flood likelihood. The ensemble likelihood is evaluated on two test sites in Myanmar and Somalia showcasing the performance during an actual flood event and an area with challenging conditions for SAR-based flood detection. The findings further elaborate on the statistical robustness when aggregating multiple likelihood layers. The final GFM ensemble likelihood layer serves as a simplified appraisal of trust in the ensemble flood extent detection approach. As an ensemble likelihood, it provides more robust and reliable uncertainty information for the flood detection compared to the usage of a single algorithm only. It can therefore help interpreting the satellite data and consequently to mitigate the effects of flooding and accompanied damages on communities and individuals

GFM Product User Manual

This Product User Manual (PUM) is the reference document for all end-users and stakeholders of the new Global Food Monitoring (GFM) product of the Copernicus Emergency Management Service (CEMS). The PUM provides all of the basic information to enable the proper and effective use of the GFM product and associated data output layers. This manual includes a description of the functions and capabilities of the GFM product, its applications and alternative modes of operation, and step-by-step guidance on the procedures for accessing and using the GFM product