Rapid mapping of disasters using any kind of satellite imagery is a challenge. The faster the response, the better the service is for the end users who are managing the emergency activities. Indeed, production rapidity is crucial whatever the satellite data in input. However, the speed of delivery must not be at the expense of crisis information quality. The automated flood and fire extraction pipelines, presented in this technical note, make it possible to take full advantage of advanced algorithms in short timeframes, and leave enough time for an expert operator to validate the results and correct any unmanaged thematic errors. Although automated algorithms aren’t flawless, they greatly facilitate and accelerate the detection and mapping of crisis information, especially for floods and fires. ExtractEO is a pipeline developed by SERTIT and dedicated to disaster mapping. It brings together automatic data download and pre-processing, along with highly accurate flood and fire detection chains. Indeed, the thematic quality assessment revealed F1-score values of 0.91 and 0.88 for burnt area and flooded area detection, respectively, from various kinds of high- and very-high- resolution data (optical and SAR).

The INterpolated FLOod Surface (INFLOS) tool was developed to meet the operational needs of the Copernicus Emergency Management Service (CEMS) Rapid Mapping (RM) component, which delivers critical crisis information within hours during and after disasters. With increasing demand for accurate and real-time flood depth estimates, INFLOS provides a rapid, adaptable solution for estimating floodwater depth across diverse flood scenarios, using remotely sensed data and high-resolution Digital Terrain Models (DTMs). INFLOS calculates flood depth by interpolating water surface elevation from sample points along flooded area boundaries, derived from satellite imagery. This tool is capable of delivering flood depth estimates in a rapid mapping context, leveraging a multistep interpolation and filtering process for improved accuracy. Tested across fourteen regions in Europe and South America, INFLOS has been successfully integrated into CEMS RM operations. The tool’s computational optimisations further enhance efficiency, improving computation times by up to 15-fold, compared to similar techniques. Indeed, it is able to process areas of up to 6000 ha in a median time of 5.2 min, and up to 30 min at most. In conclusion, INFLOS is currently operational and consistently generates flood depth products quickly, supporting real-time emergency management and reinforcing the CEMS RM portfolio.

The provision of geospatial information covering an ongoing catastrophic event can be crucial for crisis managers to locate and measure the scale of a disaster, especially for widespread disasters. Of course, the faster the response, the better the service is for the end users who are managing the emergency activities. Satellite imagery is part of an overall response in providing crisis geo-information. These systems are greatly expanding their capacities and capabilities, often leading to the major challenge of mapping many disasters, frequently and over vast areas with high- to very-high- resolution acquisitions. This means handling huge volumes of heterogeneous satellite data.Also, rapidity must not come at the expense of quality. Hence, ICube-SERTIT’s Rapid Mapping Service (RMS), specialized in the rapid delivery of crisis information since the early 2000s, implements its user-oriented Quality Management System and invests much energy in continuously improving the service. Here, ICube-SERTIT presents a number of cutting edge processes that have been developed to address this paradigm of working faster, better with increasing data volumes.ExtractEO is a software developed by ICube-SERTIT to harbor automated flood and fire extraction pipelines dedicated to disaster mapping. It makes it possible to take full advantage of advanced algorithms in short timeframes, and leave enough time for an expert operator to validate the results and correct any unmanaged thematic errors. Although automated algorithms aren’t flawless, they greatly facilitate and accelerate the detection and mapping of crisis information, especially for floods and fires. New modules and technologies are upgrading existing modules some of which are briefly presented below.Flooding often occurs during cloudy weather leading to the use of all-weather SAR data. However, a major drawback can be that extracting urban floods with SAR data is more complicated if not impossible. It has been found that flood inference can be attained by combining InSAR with AI technologies to map flooded urban areas. In a post-processing this information can then become an input with other sources for hydro-geomorphological modelling in urban areas thanks to very high resolution Digital Elevation Models (DEM), for example derived from LiDAR. Additional model inputs can be derived from gauges, social networks, and other external sources.Ground movements can have a major impact on man-made structures linked to earthquakes, landslides or volcanic activities. ICube-SERTIT has developed time-efficient inSAR pipelines, aiming to provide quick answers concerning the main ground movements with an idea of the z-direction and magnitude. In parallel, after a few days processing, using a SAR image-stack over the same area, slight changes can be measured, down to a few millimetres, thanks to Persistent Scatterer Interferometric (PSI) technology.This work is not scientifically ground-breaking except for the focus on their integration into fast rapid mapping workflows with the aim of improving the information available to users during or immediately after disasters. Like all of these implementations and endeavours, they require further specification and validation with users. Furthermore, choices need to made on what can be profitably integrated into ExtractEO.

Wildfire detection using satellite images is a widely studied task in remote sensing with many applications to fire delineation and mapping. Recently, deep learning methods have become a scalable solution to automate this task, especially in the field of unsupervised learning where no training data is available. This is particularly important in the context of emergency risk monitoring where fast and effective detection is needed, generally based on high-resolution satellite data. Among various approaches, Anomaly Detection (AD) appears to be highly potential thanks to its broad applications in computer vision, medical imaging, as well as remote sensing. In this work, we build upon the framework of Vector Quantized Variational Autoencoder (VQ-VAE), a popular reconstruction-based AD method with discrete latent spaces, to perform unsupervised burnt area extraction. We integrate VQ-VAE into an end-to-end framework with an intensive post-processing step using dedicated vegetation, water and brightness indexes. Our experiments conducted on high-resolution SPOT-6/7 images provide promising results of the proposed technique, showing its high potential in future research on unsupervised burnt area extraction.

The Rapid Mapping (RM) component of the Copernicus Emergency Management Service (CEMS) delivers crisis information within hours or days, during and in the immediate aftermath of a disaster. Users of the service have requested the incorporation of flood depth estimates for inundations and related disasters, such as storms and tsunamis. This demand required the development of a quick and operational approach for estimating floodwater depth distribution, which can effectively adapt to a wide range of flood scenarios, remotely sensed data and Digital Terrain Models (DTMs). INFLOS is a tool developed to answer these requirements, within an expedient 30-minute timeframe at most. It uses sample points collected along the edges of flooded areas derived from remotely sensed imagery, and integrates high resolution DTMs and hydrography data to enhance accuracy. Flood depth is then estimated through a multi-step interpolation and filtering process. INFLOS underwent thorough testing across fourteen regions. As it transitioned into production, INFLOS successfully generated a wealth of flood depth products accessible through the CEMS RM portal: https://emergency.copernicus.eu/mapping/list-of-activations-rapid. Currently, INFLOS is operational and its computation time remains consistently within the order of minutes, making it a staple of the RM tooling and portfolio.