Wildfire Defender Aerial Imagery Analysis System
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Solution Overview
Problem
Fire safety codes in fire-prone areas can change over time due to tree growth and new vegetation, leading to varying wildfire risks that existing technologies struggle to accurately assess and predict.
Innovation Solution
A system utilizing image processing and supplemental data, combined with artificial intelligence and machine learning, to analyze aerial imagery and property data, creating three-dimensional models and predicting wildfire spread patterns, thereby enhancing wildfire risk assessment and alert systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fire safety codes are used to assess wildfire risk, then initial compliance with separation distance requirements is maintained, but the system cannot detect changes over time due to tree growth and new vegetation
Solution Approach 1:
The system performs preliminary actions by establishing baseline aerial imagery and three-dimensional models of properties against which future changes can be detected. This preliminary characterization enables the system to automatically identify when tree growth or new vegetation reduces separation distances, allowing proactive risk assessment before compliance issues arise.
Solution Approach 2:
The system implements continuous feedback loops by repeatedly capturing aerial imagery, updating three-dimensional models, and comparing current vegetation states against fire safety code requirements. This feedback mechanism automatically detects changes in separation distances and alerts property owners when compliance is no longer met, enabling timely remediation.
2Reliability
If manual monitoring of property vegetation is performed, then compliance with fire safety codes can be verified, but the process is labor-intensive and cannot provide real-time alerts
Solution Approach 1:
The system enables self-service by allowing property owners to automatically monitor their own compliance status through the platform. Aerial imagery is automatically captured and processed, three-dimensional models are generated and updated, and compliance alerts are automatically sent to property owners when vegetation changes reduce separation distances below code requirements.
Solution Approach 2:
The system replaces manual mechanical monitoring processes with automated aerial imagery capture and processing. Drones or aerial vehicles automatically capture images, which are then processed by machine learning algorithms to detect vegetation changes, eliminating the need for manual field inspections while improving both reliability and productivity.
3Measurement precision
If detailed aerial imagery and three-dimensional modeling are implemented, then wildfire risk assessment accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex task of wildfire risk assessment into distinct modular components: aerial imagery capture, image processing and feature detection, three-dimensional model generation, compliance rule evaluation, and alert generation. Each module handles a specific aspect of the process, making the overall system more manageable and maintainable while achieving high measurement precision through the integration of these specialized components.
Data Source
AI summary
A method includes accessing a first dataset including aerial imagery data, accessing a second dataset including property boundary data, and identifying property boundaries associated with a geographic area. A plurality of artificial-intelligence (AI) models are applied to the datasets to identify and compute information of interest. Based on the first dataset and constrained by the property boundaries, a building detection model can be applied to identify a building footprint, and a tree detection model can be applied to identify one or more trees. An estimated distance can be determined between each of the trees and a nearest portion of the building footprint as separation data, which can be compared to a defensible space guideline to determine a defensible space adherence score. A wildfire risk map can be generated, including the defensible space adherence score associated with the geographic area.