Remote Tree Carbon Estimation for Utility Hazard Screening
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Solution Overview
Problem
Manual inspection of electrical assets by utility personnel is inefficient, inaccurate, costly, and poses risks due to insufficient training and harsh conditions, leading to potential hazards from vegetation interference with utility assets.
Innovation Solution
A hazard tree identification system using remote sensing and AI/ML to process images from various sources, identify and classify trees, determine their heights and distances to electrical assets, and assess potential hazards, providing notifications and risk classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection methods are used to assess vegetation hazards, then personnel can directly observe and evaluate trees, but the process becomes inefficient, inaccurate, costly, and dangerous due to harsh conditions and insufficient training
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated system using LiDAR technology, optical imagery, and AI/ML algorithms. The system automatically detects, measures, and classifies trees and vegetation hazards without human personnel physically present, thereby improving both accuracy through precise measurements and efficiency through automated processing.
Solution Approach 2:
The patent introduces remote sensing technology as an intermediary between the inspector and the vegetation hazards. LiDAR sensors and cameras capture data from a distance, and AI algorithms process this data to identify hazards, eliminating the need for personnel to work in harsh conditions while maintaining detection capability.
2Loss of information
If manual sampling and measurement protocols are followed, then detailed tree data can be collected, but the process requires significant time and resources for physical visits and sample measurements
Solution Approach 1:
The patent replaces time-consuming manual measurement protocols with automated LiDAR scanning and photogrammetry. These technologies capture comprehensive 3D data of all trees in the area simultaneously, eliminating the need for physical visits to each tree while maintaining complete measurement data collection.
Solution Approach 2:
The patent employs a multi-functional remote sensing system that simultaneously performs multiple measurement tasks: tree detection, height measurement, crown width assessment, and species classification all in one operation, rather than requiring separate manual procedures for each measurement type.
3Measurement precision
If comprehensive tree measurements are taken manually, then accurate carbon biomass calculations can be performed, but the manual process of visiting locations and recording samples is resource-intensive
Solution Approach 1:
The patent replaces complex manual measurement procedures with integrated LiDAR and optical sensing systems that automatically capture all necessary dimensional data (height, crown width, trunk diameter) and use AI algorithms to calculate carbon biomass, maintaining measurement precision while reducing operational complexity.
Solution Approach 2:
The patent transforms physical measurement parameters into digital data through remote sensing. LiDAR converts physical tree dimensions into precise 3D point cloud data, which is then processed by algorithms to derive carbon biomass parameters, changing the state from physical measurement to digital computation.
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
Enhances accuracy and efficiency in identifying and managing hazardous trees near utility assets, reducing the risk of wildfires and service failures, and optimizing vegetation management.
Implementation Method 1
a LiDAR module 1605 to measure heights of the identified individual trees
Implementation Method 2
a optical module 1603 to identify individual trees in the geographic area
Data Source
AI summary
In some embodiments, a method involves receiving remote data of a geographic area, generating a canopy height model from digital surface and terrain data, identifying individual trees using a segmentation model that integrates optical and LiDAR data, determining tree heights from the canopy height model, converting heights to diameter at breast height (DBH) values, classifying tree species by segregating them by family and identifying species, calculating biomass using DBH, height, and species-specific factors, and estimating carbon dioxide sequestration based on the calculated biomass.