UAV (Unmanned Aerial Vehicles) have shown to be a valuable instrument to inspect indoor spaces, such as damaged buildings. The images collected by these platforms are useful to understand the environment, detect relevant details such as the presence of possible hazards and localize trapped people before First Responders (FR) enter inside. So far, this useful information needs to be extracted by human operators from long image and video sequences, requesting an additional effort for the rescue team on the field. In this regard, an automated procedure to automatically extract the useful information would make the use of UAV more effective, giving tangible support to FR’s prompt action.
Södertörns Brandförsvarsförbund is an End User First Responder partner in the INGENIOUS project. SBFF is one of the largest fire and rescue organisations in Sweden with 10 municipalities within their district. They have 10 career stations (personnel 24/7), 3 “part time” stations with personal on watch at home and a few totally volunteer stations mainly on the islands in the archipelago and remote areas. Totally around 450 of staff. As a fire brigade in Sweden, they attend all sorts of incidents, not only fires but also car or train incidents, suicide, search and rescue on landslide or building collapses or incidents with hazardous substances.
The Social Media App, developed by CERTH, aims to provide the INGENIOUS system with crowdsourced information by collecting in a real-time manner social media data from Twitter (tweets), analysing their textual and visual content to enrich them with additional information, and allowing the end users to view the collected and analysed tweets in a friendly user interface.
EXUS AI Labs within the INGENIOUS project is working on implementing an alert system that will inform about the health status of first responders (FR). This system monitors the vitals of the FR and when a potential risk is identified (from their physiological data), alerts are raised to inform operators to take preventive action. Several approaches have been considered and at this stage machine learning algorithms are being utilised for the task.
Rescue operations in both small-scale emergencies and major natural or man-made disasters are undoubtedly more dangerous than in the past and the needs of First Responder teams have increased. The INGENIOUS project, funded by the European Union and the Republic of Korea under the Horizon 2020 Research and Innovation Program, aims to assist First Responders to be more effective and save more lives during natural and manmade disasters and crises by exploiting novel technologies.
The MINs are one of the different autonomous aerial vehicles that are being developed within the INGENIOUS project. The main goal of the MINs, which are being developed by our partners in SINTEF, is to support the localization of FR moving in indoor dangerous and GNSS-denied environments.
Wildfires, floodings, earthquakes and man-made incidents are striking Europe on a regular basis. These crises are often unforeseen and elicit a complex response mechanism, involving different categories of first responders such as firemen, police, medical operation teams and volunteers. The participation of such heterogeneous groups with specific operational procedures and chain of command, poses real challenges in terms of coordinating actions and getting a clear overview of the emergency response.
Developing emergency response technologies for detecting and managing waterborne pathogen contamination events
During emergency response operations, first responders are at risk of being exposed to dangerous pathogens (such as norovirus, e. coli and v. cholerae) through skin contact, ingestion or inhalation. These pathogens pose a significant risk of illness, disease or even death. Currently, there are very few available field-validated technologies to assist first responders when having to operate within an environment with such dangerous pathogens.