Wildfire Risk Imaging With LiDAR Alignment and Fire Path Graphs
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Solution Overview
Problem
Current wildfire risk assessment systems lack granularity, fail to provide building-specific assessments, are insensitive to small-scale changes, and have limited accuracy in predicting fire behavior, especially under complex environments, lacking the capability to analyze high-resolution imagery and process large volumes of data.
Innovation Solution
An image analysis system using overhead and light detection and ranging (LIDAR) imagery to assess wildfire risk, applying models like ember transport and direct fire spread models to generate a graph representing vegetation, buildings, and fire pathways, providing a graphical user interface for risk scoring and mitigation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional wildfire risk assessment systems use mathematical models and GIS technologies for large-scale analysis, then the systems can process data and provide risk assessments, but the systems lack granularity and cannot provide building-specific assessments
Solution Approach 1:
The system segments the wildfire risk assessment into building-specific units by processing imagery and data at the individual building level rather than providing only large-scale regional assessments. This allows granular risk evaluation for each building while maintaining the ability to process comprehensive data across the entire service area.
Solution Approach 2:
The system transitions from two-dimensional map-based GIS analysis to three-dimensional analysis by incorporating LiDAR data, overhead imagery, and building-specific structural information. This dimensional expansion enables the system to assess risk at the building level while maintaining comprehensive coverage.
2Reliability
If current systems use limited or outdated data sets, then the systems can operate with available data, but the accuracy and relevance of predictions are limited
Solution Approach 1:
The system merges multiple data sources including overhead imagery, LiDAR data, weather data, fuel type information, and building characteristics into a unified risk assessment framework. This combination of diverse data types enhances prediction accuracy by providing comprehensive information about fire behavior and building vulnerability.
Solution Approach 2:
The system updates and refines model parameters continuously by incorporating recent wildfire data and observing fire behavior patterns. This dynamic parameter adjustment ensures the models remain accurate and relevant despite changes in fire conditions and building characteristics over time.
3Measurement precision
If existing systems lack the capability to analyze high-resolution imagery and process large volumes of data, then the systems can maintain simpler architecture, but the ability to provide granular and sophisticated wildfire assessment scoring is restricted
Solution Approach 1:
The system introduces specialized processing modules that act as intermediaries between raw data sources and risk assessment outputs. These modules include image processing components that analyze overhead and LiDAR imagery, and data fusion components that integrate multiple data types, enabling sophisticated analysis without overwhelming system complexity.
Solution Approach 2:
The system replaces traditional mechanical GIS processing with advanced computational methods including machine learning algorithms and computer vision techniques. This substitution enables automated analysis of high-resolution imagery and complex data patterns, providing precise assessment scoring through intelligent processing rather than conventional computational methods.
4Reliability
If conventional systems do not account for ember transport and spotting, then the systems can simplify fire behavior prediction, but significant causes of wildfire spreading are not captured
Solution Approach 1:
The system performs preliminary analysis of fire behavior patterns and ember transport mechanisms before generating risk assessments. By pre-processing fire behavior data and identifying key factors such as wind conditions, terrain slope, and vegetation characteristics, the system can accurately predict ember transport and spotting without adding excessive complexity to the overall assessment process.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables granular, building-by-building wildfire risk assessment with increased accuracy and reliability, accounting for small-scale changes and incorporating high-resolution imagery, empowering homeowners and insurers with proactive risk mitigation strategies.
Implementation Method 1
at least one light detection and ranging (LIDAR) image associated with the geographic location
Data Source
AI summary
Methods, non-transitory computer-readable media, and property analysis systems are disclosed that extract vegetation location information from overhead and LIDAR images associated with a geographic location. The images are aligned based on the extracted vegetation information. Models are applied to the aligned images based on vegetation height information extracted from the LIDAR images. A graph is then generated based on a result of the application of the models. The graph represents a relationship between vegetation, one or more buildings, and one or more fire pathways associated with the geographic location. A wildfire risk score generated based on the graph is then output for the buildings via a GUI. Thus, the disclosed technology applies wildfire spread models and graphs to vegetation and buildings identified in aligned overhead and LIDAR imagery to provide relatively accurate wildfire risk assessment to insurers and homeowners and thereby facilitate informed decision-making and preventative measures.