AI Forest Fire Risk Prediction and Autonomous Aerial Detection
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Solution Overview
Problem
Existing wildfire detection systems often fail to detect fires in a timely manner due to reliance on human analysis of image data, leading to delayed responses when fires have already spread widely.
Innovation Solution
A system utilizing artificial intelligence (AI) and autonomous aerial vehicles (AAVs) to predict geographic wildfire risks using satellite data and control AAVs for real-time monitoring and detection of fire outbreaks, combining machine learning models for risk area prediction and onboard fire/smoke identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human analysts manually analyze image data for fire detection, then the system can identify fire indicators, but the detection is delayed and fires may already be widespread before action is initiated
Solution Approach 1:
The patent replaces the mechanical system of manual human analysis with an automated computer-based image analysis system. The computer system automatically processes satellite imagery and detects fire indicators without human intervention, thereby eliminating the time delay associated with manual analysis while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary detection actions by continuously monitoring satellite imagery for fire indicators before fires become widespread. The automated system is ready to detect and alert on emerging fires at their earliest stages, enabling preventive or early intervention measures before the fire grows beyond control.
2Reliability
If autonomous aerial vehicles monitor all forest areas, then complete fire detection coverage is achieved, but flight time over non-risk areas increases and resource allocation becomes inefficient
Solution Approach 1:
The patent applies local quality by directing autonomous aerial vehicles to monitor only specific high-risk areas identified through satellite image analysis. Instead of uniform monitoring across all forest areas, the system concentrates resources on locations where fire indicators are detected, thereby maintaining reliable detection coverage while improving resource allocation efficiency.
Solution Approach 2:
The satellite-based image analysis system serves as an intermediary that identifies high-risk areas, which then guides the deployment of autonomous aerial vehicles. This intermediary layer filters and prioritizes monitoring locations, enabling the AAVs to focus their efforts on areas most likely to contain fires rather than searching entire forest regions.
3Loss of time
If satellite data and machine learning models are used to predict risk areas, then navigation instructions can be optimized, but the system complexity increases
Solution Approach 1:
The system performs preliminary risk assessment by analyzing satellite imagery and using machine learning models to predict high-risk areas before deploying autonomous aerial vehicles. This advance preparation creates optimized navigation instructions that guide AAVs directly to areas of concern, reducing flight time over non-risk areas despite the added complexity of the prediction system.
Data Source
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AI summary
Complementary systems (100, 200), methods and computer programs product are disclosed for detecting an outbreak of fire in a forest-like-area. A first system (100) has a machine learning model (111) trained - based on satellite images - to predict geographic risk areas (RA1, RA2) with a forest fire risk. The predicted risk areas to be used as part of navigation instructions (10) for one or more autonomous arial vehicles (30) to observe such risk areas. The output of the machine learning model (111) is obtained by the second system (200) which is an onboard system of an autonomous arial vehicle (30) and used by a flight navigation component (210) to navigate the vehicle to and within the airspace above one of the predicted risk areas where the vehicle captures images. The second system has an onboard machine learning model (230) trained to identify real-life artefacts fire and/or smoke in the captured images. In case a predicted fire-smoke indicator (FSI) indicates the presence of fire and/or smoke artefacts, additional navigation instructions (11) are generated for the autonomous aerial vehicle (30) to approach the airspace above the location of origin of the detected fire and/or smoke artefacts to verify whether the fire and/or smoke artefacts are caused by an outbreak of fire.