We use AI modeling to improve predictable future dynamics. Our approach is a more realistic attempt to account for all factors, as our models are based on conditional data that leads to predicting where and how fast a fire is most likely to spread.
Assessing weather and sensor data.
Assessing fuel type.
Analytics & Satellite (including Enhanced Dashboard).
Overlay of 1 & 2 (including additional full predictive models & dashboard).
This will include additional AI/ ML models use in predicting and setting pre-fire priorities for resource management, for example;
Archaeological and historical areas.
OUTCOMES: We’re building on a strong foundation of geo-observations; measurements of forest structure, our AI solution will provide a leap forward in the quantity, accuracy, and resolution of measurements of vegetation structure, atmospheric contributors and terrain conditions, compared to those previously available, ultimately improving predictions of terrestrial ecosystem dynamics.
We seek to better understand the environmental and social conditions that cause forest fires, enhance planning and execution of resources by governmental authorities and fire fighters, creating a better notification and performance process for pre-fire planning, controlled fire management and population movement.
When we understand the environmental and social conditions that cause forest fires, we can enhance planning and execution of resources by governmental authorities and fire fighters, creating a better notification and performance process for pre-fire planning, controlled fire management and population movement.
Machine learning models that provide the ability to predict a forest fire and its path;
A central hub that orchestrates satellite imagery, weather data, etc.; and
Intelligence and predictive analytics services.
We will use out sensor data combined with satellite and weather aims to create a state of predictive opportunity that promotes prevention rather than cure.
We chose to take this project on because of the poor State of current systems (and our defensibility)
What is currently available in the market has yet perfected the ML opportunity.
Data is macro and not localized enough.
We are bringing to the table are the preventive possibilities found in AI coupled with proactive measurements missing in the market.
Dashboard Analytics: To help battle against forest fire destruction, using the same partnerships to help local, state and federal government entities via subscription and licensing fee model. Provide up to date monitoring in using network of Ground Devices connected to Google IOT cloud. The analytics dashboard incorporates the results from the machines learning models. The Ground Devices with have software monitoring system showing risk levels that access weather, satellite/ LIDAR data which are used in analytics for risk profiling of potential fires.
Mobile App: In supporting the above a mobile app is created as value-add service.
Intelligent Forest Fire Prediction and Prevention (IFP2) Platform: A federated integrated turnkey solution as managed services can be provide at the federal and state level.
Proactive Planning: Preventative actions in partnership with government entities under service agreement will use the historic analysis, construct near real-time predictions models used for future planning on prioritization of Timber Harvesting Plans (THP for Logging), Clearing Defensible Spaces, Planned Prescribed Control Burns, Fuel Hazard Vegetation Management and Reforestation Planting Programs.