Vegetation Imaging and LiDAR Risk Prediction for Property Hazards
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Solution Overview
Problem
Conventional systems are inadequate in identifying potential risks and preventative measures for properties due to trees and overgrown vegetation, failing to account for real-time weather data and tree attributes, and do not provide effective recommendations for reducing damage from perils such as falling trees, wildfires, flooding, and pest infestation.
Innovation Solution
A computer-implemented method utilizing imaging, LiDAR, and sensor data, combined with machine learning, to predict risks and generate actionable recommendations for mitigating or preventing hazards, including tree trimming, vegetation management, and proactive asset management strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems are used to identify potential risks and preventative measures, then system simplicity is maintained, but risk identification accuracy and effectiveness deteriorate
Solution Approach 1:
The patent combines multiple data sources (imaging data, LiDAR data, sensor data, weather data, historical data) and conventional risk assessment methods into a unified machine learning-based system. This integration allows the system to process diverse inputs simultaneously, improving risk identification accuracy while managing complexity through a consolidated analytical framework.
Solution Approach 2:
The machine learning model serves as an intermediary between raw multi-source data and risk assessment outcomes. The model processes and synthesizes complex inputs from imaging, LiDAR, sensors, and historical records, transforming them into actionable risk predictions and preventative recommendations without requiring direct complex interactions between all data sources.
2Measurement precision
If real-time imaging, LiDAR, and sensor data are processed to predict risks, then prediction accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of imaging, LiDAR, and sensor data by extracting relevant features and characteristics before feeding them into the machine learning model. This pre-processing step prepares data in advance, reducing the computational burden during real-time prediction and minimizing processing delays while maintaining accuracy.
Solution Approach 2:
The data processing pipeline is segmented into distinct stages: data acquisition from multiple sources, preliminary feature extraction, machine learning prediction, and recommendation generation. This segmentation allows parallel processing of different data types and optimizes computational resource allocation at each stage, reducing overall processing time.
3Reliability
If comprehensive vegetation analysis and risk prediction systems are implemented, then property protection effectiveness improves, but implementation cost and system complexity increase
Solution Approach 1:
The machine learning-based system is designed to perform multiple functions: analyzing imaging data, processing LiDAR data, interpreting sensor readings, predicting various types of risks (tree failure, wildfire, flooding, pests), and generating preventative recommendations. This multi-functionality consolidates what would otherwise require separate systems into a single unified platform, improving protection effectiveness while managing implementation complexity.
Data Source
AI summary
Systems and methods for analyzing vegetation are disclosed. The method may include, such as by one or more processors, transceivers, and/or a machine learning model: (1) receiving real-time data associated with a property of a user from one or more data sources, wherein the data includes one or more of image or LiDAR data for the property; (2) inputting the real-time data into the machine learning model to generate a prediction of one or more hazards to the property, wherein the machine learning model is a trained machine learning model that processes historical data to learn associations indicative of the one or more hazards to the property; (3) generating one or more recommended actions for reducing at least one of the one or more hazards; and/or (4) determining a completion of the one or more recommended actions based upon real-time response data associated with the one or more recommended actions.