LiDAR Vegetation Segmentation for Individual Tree Identification
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Solution Overview
Problem
Existing LiDAR systems fail to accurately identify the location and attributes of individual trees and other vegetation, leading to inaccurate extrapolation of sample data to larger vegetation sets due to high variability in species and attributes across geographic locations.
Innovation Solution
A method using LiDAR data processing to identify the location and attributes of individual vegetation items by selecting coordinate positions, determining if they are within allocated geographic areas, and generating digital representations to differentiate between items, enabling the identification of species through crown and branch analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LiDAR scanning is performed at high sampling intensity to capture individual vegetation items, then measurement precision is improved, but device complexity and data processing complexity increase
Solution Approach 1:
The patent segments the continuous LiDAR point cloud data into discrete individual vegetation items by identifying spatial gaps and clustering points that belong to the same vegetation object. This segmentation transforms the complex raw data into manageable individual item representations that can be processed and analyzed separately, resolving the contradiction between high sampling intensity and processing complexity.
Solution Approach 2:
The patent introduces intermediate processing steps including coordinate system transformations, point cloud filtering, and geographic area allocation as intermediary processes between raw LiDAR data collection and final vegetation attribute identification. These intermediaries simplify the data structure progressively, making the overall system more manageable despite the high sampling intensity required for precise identification.
2Device complexity
If manual sampling is used to obtain vegetation attributes, then device complexity is reduced, but measurement precision and reliability deteriorate due to extrapolation errors
Solution Approach 1:
The patent replaces the manual mechanical sampling process with an automated optical LiDAR scanning system. The LiDAR system uses laser pulses to automatically collect three-dimensional spatial data and reflective intensity data of vegetation, eliminating the need for manual measurement and extrapolation. This substitution dramatically improves measurement precision while the automated processing algorithms keep the overall system complexity manageable.
Solution Approach 2:
The patent changes the fundamental parameters of data collection from manual visual estimation and physical measurement to automated optical ranging and intensity detection. By measuring time delay of laser pulse returns for distance calculation and analyzing reflected signal intensities for vegetation characteristics, the system obtains precise quantitative data without manual intervention, resolving the contradiction between simplicity and precision.
3Loss of information
If LiDAR data is processed to identify individual vegetation location and attributes, then information completeness is improved, but loss of time in data processing increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing the LiDAR point cloud data through coordinate system transformations, noise filtering, and initial clustering before detailed vegetation attribute analysis. Geographic areas are pre-allocated to vegetation items based on spatial distribution patterns. These preliminary steps organize the data structure in advance, reducing the computational burden during subsequent detailed analysis and attribute extraction, thus mitigating processing time delays.
Solution Approach 2:
The patent implements a multi-pass processing approach where essential vegetation attributes are identified in a first pass through the data, and more detailed analysis is performed only on items requiring further characterization. This partial action strategy processes the most critical information quickly while allowing optional deeper analysis of specific items, reducing overall processing time while maintaining information completeness for essential attributes.
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
This approach allows for precise identification of individual vegetation items and their attributes, improving the accuracy of vegetation characterization and inventory management by enabling the differentiation of species based on LiDAR data analysis.
Implementation Method 1
The distance to the target location may be quantified by measuring the time delay between transmission of the pulse and receipt of one or more reflected return signals
Implementation Method 2
Light Detection and Ranging ('LiDAR') is an optical remote scanning technology used to identify distances to remote targets
Implementation Method 3
a target on the ground will reflect return signals in response to a laser pulse with varying amounts of intensity
Data Source
AI summary
Aspects of the present invention are directed at using LiDAR data to identify attributes of vegetation. In this regard, a method is provided that identifies the location of individual items of vegetation from raw LiDAR data. In one embodiment, the method includes selecting a coordinate position represented in the LiDAR data that generated a return signal. Then, a determination is made regarding whether the selected coordinate position is inside a geographic area allocated to a previously identified item of vegetation. If the selected coordinate position is not within a geographic area allocated to a previously identified item of vegetation, the method determines that the selected coordinate position is associated with a new item of vegetation. In this instance, a digital representation of the new item of vegetation is generated.