The global problem
4 – 5 million
400 – 500 million ha
300 000 deaths
500 million tonnes of CO2 emitted
(accounts for 15% global CO2 emissions).
Cost in total annually: $91 billion
Unfortunately rescue teams have to rely on outdated technology in situations where every wasted minute can cost human lives.
KrattWorks is here to change things with machine learning and AI technology.
We eliminate two of the most crucial problems for rescue teams – outdated technology and lack of situational awareness.
Drones monitor the wildfire area and stream data to the server.
We mix surveillance data from drones with satellite imagery, weather maps and other available information.
We generate accurate situational maps in real time and predict behaviour of wildfire for up to 8 hours.
This enables rescue teams to make precise decisions even before the situation begins to escalate.
Unlimited range of operation within mobile network area.
Real-time data sharing to multiple recipents.
KrattWorks has developed a smart drone controller with a mobile communication module and artificial intelligence.
Machine vision and automated object detection.
Constant situational awareness – broad picture updates automatically.
It allows for operating drones over an unlimited range, using machine vision & AI on the board of the flying drone and distributing the collected data to multiple users in different rescue teams, mission control centre and authorized 3rd parties.
Gimbal with KrattWorks machine vision module.
IR + EO gimbal with integrated machine vision module offers machine vision capability to the drones you already have!
Fire front mapping from uploaded video file.
KrattWorks is pleased to offer fire front mapping service from drone thermal videos.
The service works today with DJI drones. Based on the drone gimbal telemetry data we can calculate where on the ground the fire front is located and export this information as *.kml file.
Drone with on-board KrattWorks machine vision module.
Independently operating system that monitors the designated area and analyses visible imagery.
The processed data of fireline location, intensity and movement is combined with other map layers for output. This gives firefighting teams automatically updated situational maps in real time.