Remote Sensing Tree Risk Prediction for Power Line Vegetation
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Solution Overview
Problem
Conventional methods for predicting vegetation growth and its impact on power facilities face challenges in accurately identifying tree species and classifying vegetation, leading to difficulties in estimating future growth risks and increasing costs due to wide-area monitoring and data processing.
Innovation Solution
A vegetation management system that classifies trees based on growth activity using remote sensing data and machine learning, predicting future growth and determining contact risks with power facilities through a system comprising data acquisition, classification, growth prediction, risk determination, and visualization units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed tree species identification is performed using conventional methods, then measurement precision is improved, but productivity deteriorates due to extreme difficulty even for specialists
Solution Approach 1:
The patent replaces manual mechanical inspection by specialists with automated optical/image-based detection systems. The system uses satellite images, aerial photographs, and drone-captured images combined with machine learning algorithms to automatically identify tree species and assess vegetation risks, eliminating the need for human specialists to physically inspect each tree while maintaining or improving identification accuracy.
Solution Approach 2:
The patent creates digital copies of vegetation through remote sensing imagery (satellite images, aerial photographs, drone images) and uses these copies for analysis instead of requiring physical inspection. The machine learning model processes these image copies to identify tree species and assess risks, enabling automated decision-making without human intervention in the field.
2Reliability
If wide-area monitoring frequency is increased, then reliability is improved, but loss of energy deteriorates due to extremely increased costs
Solution Approach 1:
The patent implements periodic monitoring using time-series analysis of satellite images and aerial photographs. Instead of continuous monitoring, the system analyzes vegetation at regular intervals, using machine learning to detect changes and assess growth trends. This periodic approach maintains reliability by capturing vegetation status at multiple time points while significantly reducing the energy and cost burden compared to continuous monitoring.
Solution Approach 2:
The patent applies partial monitoring by focusing computational resources and analysis on areas with higher risk or greater vegetation change. The machine learning model identifies and prioritizes regions where vegetation contact risk is elevated, allowing the system to maintain high reliability for critical areas while reducing monitoring intensity in low-risk areas, thereby optimizing energy consumption and costs.
3Measurement precision
If three-dimensional measurement using LIDAR sensor is performed, then measurement precision is improved, but device complexity deteriorates due to difficulty in vegetation classification
Solution Approach 1:
The patent merges multiple data sources including satellite images, aerial photographs, drone images, and LIDAR three-dimensional measurement data into a unified analysis framework. The machine learning model processes these combined data types together, allowing the system to leverage the height measurement precision of LIDAR while compensating for its classification limitations using complementary information from optical imagery and spectral data.
Solution Approach 2:
The patent creates a composite information structure by combining multiple types of remote sensing data (optical images, spectral data, three-dimensional LIDAR measurements) into a unified dataset for machine learning analysis. This composite approach allows the system to overcome the limitations of any single data type, using LIDAR for precise height measurement while using optical and spectral data for vegetation classification and species identification.
Data Source
AI summary
It is difficult to predict an influence of vegetation on a feature with high accuracy. Accordingly, in an embodiment, a vegetation management system, that manages an influence of vegetation on a predetermined feature, includes: an acquisition unit that acquires remote sensing image data of the vegetation; a classification unit that classifies, based on the remote sensing image data, a tree included in the vegetation in accordance with growth activity representing potential for future growth; a growth prediction unit that predicts growth of the tree based on a classification result obtained by the classification unit; a risk determination unit that determines risk of contact with the predetermined feature; and a visualization unit that outputs and visualizes a determination result obtained by the risk determination unit.