Wildfire Risk Mapping With Aligned LiDAR and Overhead Imagery
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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fire behavior, especially under complex environments, and do not account for ember transport and spotting, relying on outdated data sets.
Innovation Solution
An image analysis system using overhead and light detection and ranging (LIDAR) imagery to assess wildfire risk by aligning images based on vegetation information, applying models like ember transport and direct fire spread to generate a graph representing relationships between vegetation, buildings, and fire pathways, and outputting a wildfire risk score via a GUI.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional remote sensing and GIS technologies are used to map fuel types and vegetation, then large-scale analysis is achieved, but building-specific granularity and sensitivity to small-scale changes are lost
Solution Approach 1:
The system segments the analysis into multiple levels: region-level overview and building-specific detailed assessments. Each building is analyzed individually using high-resolution imagery, while still maintaining the ability to aggregate results for larger area assessments. This segmentation enables both granular building-level precision and scalable regional coverage.
Solution Approach 2:
The system transitions from traditional 2D satellite imagery to 3D point cloud data derived from LiDAR. This dimensional enhancement allows the system to capture vertical vegetation structure, building heights, and spatial relationships that were previously invisible, enabling granular building-specific assessments while maintaining broad coverage through efficient 3D data processing.
2Reliability
If conventional wildfire risk models are used, then general fire behavior prediction is achieved, but accuracy under complex environments and sensitivity to small-scale changes deteriorates
Solution Approach 1:
The system introduces an intermediary processing layer that bridges simple risk models and complex environmental data. This layer processes high-resolution 3D vegetation and building data to generate enhanced input parameters for fire behavior models, improving their accuracy in complex environments without requiring complete model redesign. The intermediary layer translates detailed spatial data into model-appropriate formats while preserving critical small-scale features.
Solution Approach 2:
The system changes the parameters fed into fire behavior models from conventional low-resolution inputs to high-resolution 3D parameters including vegetation height, density, building geometry, and precise spatial relationships. These parameter changes enable standard models to achieve higher predictive accuracy in complex environments by providing them with previously unavailable detailed information about fuel loads and building vulnerabilities.
3Reliability
If existing assessment systems are used, then general wildfire risk is evaluated, but ember transport and spotting are not accounted for
Solution Approach 1:
The system performs preliminary analysis of vegetation and building spatial relationships before applying fire behavior models. It pre-identifies potential ember transport pathways, spotting zones, and vulnerable building areas based on 3D spatial configuration. This preliminary action enables the incorporation of ember transport and spotting effects into the overall risk assessment without requiring complete real-time modeling of these complex processes during fire events.
4Quantity of substance
If conventional systems are used, then processing speed is maintained, but ability to analyze high-resolution imagery and process large volumes of data deteriorates
Solution Approach 1:
The system segments large volumes of high-resolution imagery and LiDAR data into manageable building-specific subsets. Each building's risk assessment processes only its relevant local data (vegetation within specific distance buffers, adjacent structures, local topography), rather than analyzing entire regional datasets. This segmentation enables parallel processing of multiple buildings simultaneously, maintaining high processing speed while handling large total data volumes.
Solution Approach 2:
The system extracts only the critical data elements needed for each building's risk assessment from the vast amounts of available imagery and LiDAR data. It identifies and extracts vegetation features, building characteristics, and spatial relationships relevant to fire risk, discarding or storing only essential information. This extraction approach reduces processing requirements while preserving the detailed information necessary for accurate building-specific assessments.
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
Provides granular, building-specific wildfire risk assessments that are sensitive to small-scale changes, improving predictive accuracy and reliability by considering various factors, enabling informed decision-making for mitigation and preparedness.
Implementation Method 1
light detection and ranging (LIDAR) imagery
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.