Vegetation Contact Risk Analysis Using Remote Sensing
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Solution Overview
Problem
Existing vegetation management systems that use remote sensing data to analyze the risk of vegetation contact with facilities face high costs due to the need for extensive three-dimensional measurements and frequent data collection.
Innovation Solution
A vegetation management system that classifies vegetation using remote sensing data, predicts long-term and short-term wide-area fluctuations, determines contact risks, and visualizes results to support maintenance work without relying on extensive three-dimensional data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional measurement utilizing LiDAR sensor is performed to accurately determine vegetation contact, then measurement precision is improved, but cost increases extremely
Solution Approach 1:
The invention extracts only the essential information needed for vegetation contact determination from remote sensing data, rather than performing comprehensive three-dimensional measurements. By focusing on specific vegetation parameters and contact risk indicators, the system achieves accurate determination without the extreme cost of full LiDAR-based 3D mapping.
Solution Approach 2:
The system uses cost-effective remote sensing imagery instead of expensive LiDAR sensors for vegetation contact determination. By leveraging widely available satellite or aerial imagery that can be frequently updated, the system maintains measurement precision while dramatically reducing the cost per measurement cycle.
2Ease of operation
If three-dimensional conversion of facility and vegetation is performed to support visualization and intuitive operation, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The system creates simplified two-dimensional representations of vegetation and facilities that preserve the essential spatial relationships needed for contact determination. Instead of converting all data to three dimensions, the invention uses 2D imagery with overlaid analysis results that provide intuitive visualization while avoiding the complexity of full 3D modeling.
Solution Approach 2:
The invention compensates for the lack of three-dimensional conversion by utilizing the vertical dimension in a different way - analyzing vegetation height and growth patterns in the vertical direction through remote sensing indices, while maintaining two-dimensional spatial representation for visualization. This approach provides intuitive operation without requiring complex 3D data structures.
3Measurement precision
If frequent photographing is performed to perform accurate vegetation contact determination, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The system implements periodic monitoring using remote sensing data at optimized intervals, rather than requiring frequent photographing. By analyzing vegetation growth rates and contact risk over time periods, the system achieves accurate determination while maintaining high productivity through efficient use of available remote sensing passes.
Solution Approach 2:
The system uses the natural periodicity of remote sensing satellite passes and vegetation growth cycles to its advantage. Instead of actively conducting frequent surveys, the system analyzes routinely captured remote sensing data that self-updates over time, achieving both measurement precision and productivity by leveraging the self-service nature of continuous remote sensing coverage.
Data Source
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AI summary
A vegetation management system 1 includes a vegetation classification unit 13 that classifies vegetation photographed in remote sensing data, a long-term change prediction unit 15 that predicts, based on a classification result of the vegetation and the remote sensing data, long-term wide-area fluctuation that is range fluctuation of the vegetation in a predetermined long-term time sequence, a short-term change prediction unit 16 that predicts, based on the classification result of the vegetation and the remote sensing data, short-term wide-area fluctuation that is range fluctuation of the vegetation in a predetermined short-term time sequence, a risk determination unit 17 that determines, based on a prediction result by long-term change prediction unit 15 and a prediction result by short-term change prediction unit 16, a risk due to contact of the vegetation and a facility, and a visualization unit 18 that visualizes a determination result of the risk.