ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management

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)

ExtractEO, a Pipeline for Disaster Extent Mapping in the Context of Emergency Management

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)

GMES Emergency Response - Two years of EO-based Rapid Mapping

Within the 7th research framework programme of the European Commission the SAFER project aims at developing and providing a pre-operational version of the GMES Emergency Response Service (ERS). A main component of this is the rapid mapping service called Emergency Mapping. Within this service satellite-based information products are generated covering natural or man-made disasters, like for the Haiti Earthquake, the Poland, Romanian and Pakistan floods, the Xynthia storm in France and the alkali mud flow in Hungary (examples are shown in this paper). Overall, the Emergency Service’s main objective is to set up a pre-operational service based on precursor projects’ services and user collaboration to set in motion the basis for future operational environments in the GMES Initial Operations (GIO) and further on in a fully operational context. Besides the regular service provision which increased from 22 activations in year 1 to 51 in year 2, the service focuses on further improving the service structure, the portfolio and the technical background to target improvements in response time and the quality of the crisis map products. In total more than 600 crisis products were generated during the first two years of GMES Emergency Mapping. Following the project’s initiation process and the setting up of the SAFER Emergency operational model, service provision is well structured and performs smoothly, benefiting from the long lasting experience of the initially involved service providers. Lessons learned during the first year were immediately analysed and resulting improvements have already been implemented

Mapping burn scars, fire severity and soil erosion susceptibility in southern France using multisensoral satellite data

This article focuses on the mapping of fire burn scars, fire severity and soil erosion susceptibility using multi-sensoral satellite data. An automatic procedure for the mapping of fire affected areas and for the estimation of fire severity using Sentinel-2 data is presented. The Sentinel-2 based classification results are compared to a burn scar derived by a semi-automatic object-based approach using Sentinel-1 amplitude and coherence time-series data systematically processed by the Sentinel-1 InSAR Browse service implemented on the Geohazard Exploitation Platform (GEP) of ESA. Further, a transferable approach to compute a soil erosion susceptibility index based on Pléiades data is presented. The SAR- and optical-data based methods are applied in a test area near Marseille/Vitrolles, France, which was affected by severe forest fires in August 2016

The Islamic State unfiltered

Instagram has become an unsuspecting pulpit ­– seemingly caught off guard – for those determined to spread a militant message of Islamic State terror. Graphic, fanatical and oftentimes heavily photoshopped images weave through Instagram’s labyrinth of sunset snaps and gym selfies to advance a curious manifestation of cause-related self-promotion