Autonomous Aerial Tree-Stand Scanning With 3D Interpolation
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Solution Overview
Problem
Existing methods for measuring tree population and health in forestry are inefficient and lack detailed, accurate data collection techniques.
Innovation Solution
A method involving an aerial vehicle that autonomously scans and constructs a three-dimensional representation of a tree stand by capturing images from multiple altitudes and interpolating tree characteristics, using computer vision and machine learning to generate a virtual representation of tree health and metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional ground-based methods are used to measure tree population and health, then detailed tree characteristics can be obtained, but the measurement process is time-consuming and inefficient
Solution Approach 1:
The patent transitions from ground-based two-dimensional measurement to aerial three-dimensional imaging, capturing tree canopy, trunk, and ground data from multiple altitudes simultaneously, thereby improving measurement efficiency without sacrificing precision
Solution Approach 2:
The patent divides the forest stand into multiple scan zones and captures images at different altitudes (canopy level, trunk level, ground level), processing each segment separately to achieve comprehensive measurement while maintaining overall efficiency
2Productivity
If aerial imaging is used to cover large forest areas, then measurement efficiency improves, but measurement precision and detail of tree characteristics deteriorate
Solution Approach 1:
The patent segments the imaging process into multiple altitude levels (canopy, trunk, ground) and multiple scan zones, allowing comprehensive data collection across large areas while maintaining detailed measurement capability at each level
Solution Approach 2:
By capturing images from multiple altitudes simultaneously, the system adds the altitude dimension to imaging, enabling both wide coverage and detailed measurement without compromising precision
3Measurement precision
If multiple scan zones and altitudes are used to capture comprehensive tree data, then measurement precision improves, but system complexity increases
Solution Approach 1:
The aerial vehicle is designed as a multi-functional platform that integrates imaging sensors, GPS positioning, and autonomous navigation, performing multiple measurement functions (canopy imaging, trunk imaging, ground imaging) simultaneously to reduce overall system complexity
Solution Approach 2:
The patent divides the forest into multiple scan zones and captures data at different altitudes, processing each segment independently through automated workflows, which manages complexity through systematic decomposition
4Productivity
If automated image processing and interpolation are used to generate virtual representations, then productivity improves, but algorithm complexity increases
Solution Approach 1:
The system implements automated image processing workflows that self-correct and self-process without human intervention, using algorithms to automatically detect features, interpolate missing data, and generate virtual representations, thereby improving productivity while managing complexity through automation
Data Source
AI summary
One variation of a method includes: accessing a boundary of a stand of trees; defining an array of scan zones within the boundary; accessing a first sequence of images representing treetops in a first scan zone; accessing a second sequence of images representing bases of trees in the first scan zone; accessing a third sequence of images representing bases of trees in a second scan zone; accessing a fourth sequence of images representing treetops in the second scan zone; interpolating canopy characteristics of trees between the first scan zone and the second scan zone based on the first and fourth sequences of images; interpolating lower tree characteristics of trees between the first scan zone and the second scan zone based on the second and third sequences of images; and compiling canopy and lower tree characteristics into a virtual representation of tree characteristics across the stand of trees.