Wildfire Smoke Recognition and Spread Forecasting With UAV Imaging
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Solution Overview
Problem
Current systems for tracking and managing large-area wildland fires face limitations in early detection, quick recognition, and effective anticipation, particularly due to issues with satellite image resolution, cloud cover, and aircraft range, which can lead to delayed response and increased fire spread.
Innovation Solution
A system utilizing self-steering unmanned aerial devices equipped with sensors and a deep-neural network for real-time image processing, combining ambient weather data and land features to automatically recognize smoke or fire signals, predict fire growth, and generate early warning signals, leveraging cloud and fog computing for efficient data processing and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If satellite images are used for fire detection, then large-area coverage is achieved, but image resolution is insufficient and cloud cover limits effectiveness
Solution Approach 1:
The system divides the monitoring task into two levels: satellite provides broad area coverage while UAVs provide high-resolution detailed monitoring of specific regions of interest. This segmentation allows each component to optimize for its specific function without compromise.
Solution Approach 2:
The system transitions from two-dimensional satellite imaging to three-dimensional monitoring by deploying UAVs that can physically approach and circle fire locations, providing multi-angle high-resolution views from different spatial dimensions.
2Measurement precision
If aircraft with sensors are used for fire detection, then high detection capability is achieved, but range and operational conditions are limited
Solution Approach 1:
The UAVs are equipped with autonomous navigation and self-steering capabilities, allowing them to independently locate and track fire sources without continuous human intervention or ground control, extending their operational independence and range.
Solution Approach 2:
The UAV system is designed to perform multiple functions including fire detection, tracking, and monitoring under various conditions, making it adaptable to different operational environments and scenarios beyond what specialized aircraft can achieve.
3Measurement precision
If deep-neural network processing is implemented for real-time fire recognition, then detection accuracy is improved, but computational requirements and system complexity increase
Solution Approach 1:
The system pre-processes satellite imagery to identify potential fire locations and regions of interest before deploying UAVs, reducing the computational burden on the deep-neural network by focusing processing only on relevant areas rather than entire satellite images.
Solution Approach 2:
The system uses intermediate processing layers that combine simplified detection algorithms with deep-neural network analysis, acting as a mediator that reduces computational complexity while maintaining high detection accuracy through progressive refinement.
Data Source
AI summary
A method and system to receive, one or more first images, one or more second images, one or more ambient weather related information, and one or more land related information, wherein the one or more land related information comprise vegetation features, terra firma topography, elevation, slope and aspect, of one or more regions of interest, of a geographical region; to map automatically, one or more risk areas of the one or more regions of interest; to recognize automatically, one or more smoke or fire related signals; and to predict computationally, existence of a fire causing smoke, a fire, a fire-growth and spread.