# ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management
## 1 Introduction  
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. With climate change, both flood and fire mega-disasters are becoming far more common and intense, affecting vast areas. Often, due to weather patterns, many events can occur simultaneously. Simultaneously, satellite systems are greatly expanding their capabilities, leading to potentially very frequent, high- to very-high- resolution acquisitions. Hence, there is a major challenge to map many disasters and to map them frequently over vast areas. This means handling huge volumes of heterogeneous satellite data. The processing and analysis of vast volumes of satellite imagery acquired just after or during a disaster requires methods and tool development to not only deal with the volume of event-associated data, but also to meet the increasing speed of delivery demands of crisis management actors. Indeed, reducing the time to map flooded or burned areas is a major issue for civil protection agencies and, consequently, for service providers with this parameter who often determine the added value of the information provided. The purpose of this technical note is to introduce ExtractEO (EEO), a pipeline sized to rapidly map widespread disasters on an industrial scale. Through pipelines like this, service providers can map mega-disasters over vast areas at high resolutions and speeds, providing unequalled service to emergency managers. ICube-SERTIT's Rapid Mapping Service (RMS) has specialized in the rapid delivery of crisis information derived from Earth Observation (EO) since the early 2000s [1; 2; 3; 4; 5; 6]. SERTIT's RMS works within many frameworks, which most notably include: the CopernicusEmergency Management Service-Rapid Mapping (CEMS-RM) (2015) for the European Commission's Joint Research Centre [7], with e-GEOS as Prime; the International Charter Space and Major Disasters (2001) for and on behalf of the CNES for the needs of French Civil Security and a number of Francophone authorized users; insurance industry companies; and local/regional authorities. SERIT is involved in both crisis activation management and geo-information production. In order to ensure an operational 24/7 front room, it is necessary to manage the availability of personnel, as well as team training on tools, methods, and protocols. The RMS follows this through a Quality Management System to make sure the service satisfies evolving user/client needs and continuously improves. Depending on the Service Level Agreement, the RMS needs to deliver rough estimates within 2-3 h, and finalized higher detail products within 7-9 h, from image reception and quality acceptance. Indeed, SERIT works tirelessly to improve and streamline workflows, algorithms, and methods, with an eye to widening the product portfolio and an intense focus on meeting user requirements. At present, to speed up the service, SERIT is introducing previously tried and tested automated methods that integrate Machine Learning and automated sampling into image processing and information extraction pipelines. In this note, we will look at how these automated methods are being improved within the particularly demanding application domain of rapid mapping. SERIT's EEO pipeline provides flood and fire extraction methods in the optical and SAR domains that help to produce crisis information layers over vast areas in a matter of minutes on a standard computer. EEO's approach is similar to the operational ESA Charter Mapper [8] developed to better support rapid mapping in the framework of the International Charter. Indeed, both applications are able to ingest a huge number of sensors in a standardized processing environment, including thematic chains. Both flood and fire automatic mapping are widely covered in the literature. For example, SAR-based flood detection is itself a huge topic.