Fire Risk Assessment Using Aerial Imagery Analysis

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Solution Overview

Problem

There is a need for accurate and reliable methods to assess fire risk to properties, particularly in wildfire-prone areas, to support insurance underwriting and compliance with local regulations, as existing methods do not adequately consider local factors influencing vulnerability and hazard.

Innovation Solution

An interactive interface using aerial images and machine learning to compute fire risk metrics by analyzing features such as vegetation, debris, and building structures, combining hazard scores with vulnerability metrics, and accounting for factors like adjacency, overlap, and topology, with real-time updates and historical data integration.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional fire risk assessment methods are used, then the assessment process is simple, but the accuracy and reliability of fire risk determination is insufficient

Engineering Contradiction:

Improvefire risk assessment accuracyVSAvoidassessment system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The fire risk assessment is divided into two independent components: wildfire hazard assessment (external factors beyond property control) and vulnerability assessment (property-specific factors). This segmentation allows each component to be evaluated separately using appropriate data sources and methods, improving overall accuracy while maintaining manageable system complexity.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system integrates multiple data sources (aerial imagery, topographic data, vegetation data, weather data) and analysis methods into a unified fire risk assessment platform that serves multiple purposes: insurance underwriting, compliance verification, and risk mitigation planning.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Reliability

If comprehensive local factors are considered in fire risk assessment, then the reliability of risk determination improves, but the complexity of data collection and analysis increases

Engineering Contradiction:

Improvefire risk determination reliabilityVSAvoiddata collection and analysis complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

Wildfire hazard data is pre-computed at the parcel level using aerial imagery, topographic data, and vegetation data before insurance applications are processed. This preliminary assessment of external hazard factors eliminates the need for insurers to collect and analyze this complex data individually, improving reliability while reducing the complexity burden on end users.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system introduces an intermediary hazard assessment layer that mediates between complex environmental factors (wildfire behavior, topography, vegetation) and property vulnerability. This intermediary computation translates complex natural factors into standardized hazard scores that can be easily integrated with property-specific vulnerability data.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Loss of information

If manual property surveys are conducted to assess vulnerability, then detailed local information is obtained, but the time and cost of assessment increases

Engineering Contradiction:

Improvelocal property information completenessVSAvoidassessment time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system creates digital copies of property characteristics through aerial imagery and automated feature extraction. Instead of requiring physical surveys to document roof materials, vegetation, and structure features, the system captures and analyzes high-resolution images to extract vulnerability data, maintaining information completeness while dramatically reducing assessment time.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

Manual field surveys are replaced with automated remote sensing and image analysis systems. Machine learning algorithms automatically identify and classify vegetation, structures, and hazard features from aerial imagery, substituting human inspectors with computational analysis to reduce time and cost while maintaining assessment quality.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

4Object-affected harmful factors

If defensible space requirements are enforced, then property vulnerability to wildfire is reduced, but the ease of operation and maintenance for property owners decreases

Engineering Contradiction:

Improvewildfire vulnerabilityVSAvoidproperty maintenance ease

Core Design Contradiction:

Object-affected harmful factorsVSEase of operation

Solution Approach 1:

The system provides automated feedback to property owners about their defensible space compliance status based on aerial imagery analysis. By clearly communicating what features contribute to vulnerability (vegetation proximity, roof materials, deck structures), the system guides owners toward cost-effective modifications that reduce wildfire risk while maintaining property functionality.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250111668A1Fire risk determination

Publication Date: 2025.04.03 NEARMAP AUSTRALIA PTY LTD

AI summary

A method for automatically determining a fire risk of a property utilizing machine learning to account for various features on a property, the machine learning automatically identifying features on a property relative to a building and providing weighting to the identified features based on various criteria to generate a fire score for the property and a total fire risk when the fire score is combined with a fire hazard associated with the property.