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The LIS-platform, a BIM-based software platform, is being developed to monitor various aspects of buildings. This platform provides a comprehensive overview of structu...
We provide a low-cost system for forest monitoring, fire prevention and detection of the cause of a fire, for all countries that currently do not have the financial, technological or institutional capacity to develop this on their own.
Developed by Heli-lab
EFESTO is an artificial intelligence system, mounted on a drone, that carries out continuous aerial monitoring of forest areas. It is able to predict the development of fires and intercept them. It can send alerts about hazardous situations by sending images and GPS coordinates directly to the smartphone of rescuers and law enforcement.
See more information about this level and the TRL and SRL levels.
A drone equipped with thermocamera, temperature and humidity sensors, and electronic probe which constantly scours the forest area and transmits a film including GPS coordinates to the artificial intelligence system. When the drone sensors detect abnormal heat sources, smoke or flames, it alerts firefighters via SMS / CALL / MAIL.Through the app, the operator can view the video in real time, evaluate the severity of the situation, identify the location of the event and alert the firefighters. During the fire extinguishing operations, the drone continues to communicate thermal imaging and GPS coordinates of critical areas to the operators in order to optimize an appropriate response.
Limitations/conditions under which this innovation does not work or is less effective
At this moment, the solution works only under coverage of 3G/LTE/5G signal but we are studying the possibility to insert a satellite SIM backup.
Added value
EFESTO, unlike existing systems, does not only focus on fire monitoring but allows them to be avoided by using complex prediction algorithms. The monitoring solution allows for economical savings of about 90% compared to current systems and a 99% reduction of CO2 emissions. In addition to being economical, the solution is simple to use and can be directly implemented by local economically and technically less advanced communities, providing a basis for community-level fire management programs.
We provide a low-cost system for forest monitoring, fire prevention and detection of the cause of a fire, for all countries that currently do not have the financial, technological or institutional capacity to develop this on their own.
Developed by Heli-lab
EFESTO is an artificial intelligence system, mounted on a drone, that carries out continuous aerial monitoring of forest areas. It is able to predict the development of fires and intercept them. It can send alerts about hazardous situations by sending images and GPS coordinates directly to the smartphone of rescuers and law enforcement.
The main components of the system have been tested separately, and an initial integration exercise has been conducted.
A drone equipped with thermocamera, temperature and humidity sensors, and electronic probe which constantly scours the forest area and transmits a film including GPS coordinates to the artificial intelligence system. When the drone sensors detect abnormal heat sources, smoke or flames, it alerts firefighters via SMS / CALL / MAIL.Through the app, the operator can view the video in real time, evaluate the severity of the situation, identify the location of the event and alert the firefighters. During the fire extinguishing operations, the drone continues to communicate thermal imaging and GPS coordinates of critical areas to the operators in order to optimize an appropriate response.
Limitations/conditions under which this innovation does not work or is less effective
At this moment, the solution works only under coverage of 3G/LTE/5G signal but we are studying the possibility to insert a satellite SIM backup.
Added value
EFESTO, unlike existing systems, does not only focus on fire monitoring but allows them to be avoided by using complex prediction algorithms. The monitoring solution allows for economical savings of about 90% compared to current systems and a 99% reduction of CO2 emissions. In addition to being economical, the solution is simple to use and can be directly implemented by local economically and technically less advanced communities, providing a basis for community-level fire management programs.
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