Tree Risk Assessment Using Multi-Scale Image Features
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Solution Overview
Problem
Existing tree risk assessment methods rely heavily on subjective expert judgment and manual surveys, leading to inconsistent accuracy and high uncertainty due to limited systematic data and ambiguous qualitative assessments.
Innovation Solution
A tree assessment method utilizing a pre-constructed tree risk assessment model with data augmentation and multi-scale feature extraction, combined with intelligent report generation to enhance accuracy and efficiency, reducing manual time and improving consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantitative risk assessment methods are used with formulas and statistical analysis, then risk levels can be calculated, but the accuracy is limited by lack of systematic data and heavy reliance on expert subjectivity
Solution Approach 1:
The patent replaces manual expert assessment (mechanical human judgment process) with an automated deep learning model that processes tree images through neural networks. The model substitutes human experts' subjective formula-based calculations with objective multi-scale feature extraction and automated risk classification, eliminating inter-expert variability while maintaining high accuracy through learned patterns from training data.
Solution Approach 2:
The patent transforms the assessment approach by changing from single-scale manual measurements to multi-scale feature extraction. The deep learning model extracts features at multiple spatial scales simultaneously, capturing both fine details (leaf-level defects) and global structures (overall tree form), thereby improving measurement precision beyond what traditional single-scale methods can achieve.
2Ease of operation
If qualitative risk assessment methods are used with rating matrices, then risk levels can be classified, but the inherent subjectivity and ambiguity reduce assessment reliability
Solution Approach 1:
The patent replaces the manual qualitative rating process with automated deep learning classification. Instead of experts subjectively assigning ratings and combining them in matrices, the model automatically classifies trees into risk categories based on learned patterns from training data, maintaining operational simplicity while eliminating subjectivity through consistent automated decision-making.
3Measurement precision
If manual surveys and inspections are conducted by tree experts, then detailed assessments can be performed, but the process is time-consuming and has inconsistent accuracy
Solution Approach 1:
The patent replaces time-consuming manual surveys with automated image-based deep learning assessment. Tree experts previously needed to physically inspect trees and record observations; now, images are automatically processed through the trained model that performs multi-scale feature extraction and risk classification in seconds, dramatically improving productivity while maintaining or enhancing accuracy through consistent application of learned patterns.
Solution Approach 2:
The patent creates a virtual copy of the expert assessment process through the deep learning model. The model learns from training data that encapsulates expert knowledge and replicates the assessment function automatically. This digital copy can process multiple images simultaneously without fatigue or variability, enabling high-volume processing while preserving the expertise embedded in the training data.
4Measurement precision
If data augmentation and multi-scale feature extraction are implemented, then assessment accuracy is improved, but model complexity increases
Solution Approach 1:
The patent applies data augmentation and multi-scale feature extraction during the training phase (preliminary action) to prepare the model for accurate predictions. By pre-processing training data through augmentation techniques and configuring the model to extract multi-scale features beforehand, the system achieves high prediction accuracy while keeping the inference process relatively simple. The complexity is paid for once during training, not during each assessment.
Data Source
AI summary
A tree assessment method and a tree assessment device are provided, including steps: acquiring a tree image set to be evaluated, wherein the tree image set to be evaluated includes a plurality of tree images to be evaluated; using a tree risk assessment model that is pre-constructed to perform a risk prediction and a multi-scale feature extraction on each of the plurality of tree images to be evaluated to obtain a risk assessment result and a multi-scale feature; generating an initial assessment report based on the risk assessment result and determining a similarity between the multi-scale feature and a target risk category feature that is pre-stored; and generating a tree assessment report based on the similarity and the initial assessment report.