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.
If you have not already read the 2nd issue of the INGENIOUS newsletter, now is the time to do it!
The Augmented Reality solutions of INGENIOUS will be presented at the online workshop titled “How can Augmented Reality enhance First Responders’ capabilities”, which will take place on April 20th 2021 between 09:50 and 12:00 am CET. The workshop is the first in a series of open workshops organised by our DRS Cluster project RESPOND-A, addressing new technologies for first responders.
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 Deployable Positioning System and MACS-SaR developed by DLR to increase the situational awareness and operational capacity of first responders
The deployable Integrated Positioning System (IPS) for self-localization and environmental mapping will track and trace first responders and offer seamless navigation indoors and outdoors in an absolute reference system, while also creating a map of the surrounding environment and detecting and tracking persons and assets in real time.
INGENIOUS will participate in the “CERIS – DRS- Discussion on Technologies for First Responders for Disaster Risk” which will take place on the 24th of February 2021 from 10h to 12h CET.
EXUS AI Labs, the R&D department of the INGENIOUS partner, EXUS, focuses on designing and developing robust and trustworthy AI solutions that allow us to leverage the untapped potential of big data analytics across multiple verticals. We impact the technological future by developing techniques to capture, process, analyse and visualise large datasets in timeframes not accessible to standard IT technologies.
We are delighted to announce that INGENIOUS will participate in the online Conference which is being organised by Public Safety Communication Europe (PSCE) on 19-20 January 2021. The INGENIOUS Consortium will present the project’s solutions related to data-driven analysis and visualization of the operational environment.