Power Line Georectification Using Neural Network Raster Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional power line extraction systems rely on rule-based algorithms that are difficult to generalize and can be inaccurate, especially when power lines are covered by vegetation or have incomplete data points, leading to errors in spatial coordinate determination.
Innovation Solution
A computer-implemented method that generates a combined raster image from point cloud data and reference data, using feature channels and binary masks to guide a neural network for accurate power line georectification, thereby overcoming the limitations of rule-based methods and improving location accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based algorithms are used for power line extraction, then the system is simple to implement, but the accuracy deteriorates when power lines are covered by vegetation or have incomplete data points
Solution Approach 1:
The patent replaces rule-based algorithms with a neural network-based deep learning system. The neural network is trained on labeled power line images to automatically learn features and patterns, substituting the mechanical rule-based approach with an intelligent system that can handle complex scenarios like vegetation coverage and incomplete data points, thereby improving accuracy while maintaining implementation feasibility through standardized training procedures
Solution Approach 2:
The patent transforms the input data by converting raw coordinate data into image representations with multiple channels (original image, absolute difference image, relative difference image). This parameter transformation allows the neural network to process spatial relationships and detect power lines more accurately, especially in challenging conditions where traditional rules fail
2Measurement precision
If on-site inspections are conducted to verify power line locations, then the accuracy of location data is improved, but the cost and time consumption increase significantly
Solution Approach 1:
The patent implements a self-service system where the neural network automatically processes and verifies power line location data without requiring human intervention for each case. The system self-corrects errors by comparing predicted locations with reference data and iteratively improving accuracy through the multi-channel image analysis, eliminating the need for time-consuming on-site inspections while maintaining high accuracy
Solution Approach 2:
The patent incorporates feedback mechanisms where the neural network's predictions are compared with reference power line locations, and the differences are used to generate correction images (absolute and relative difference images). This feedback loop allows the system to automatically identify and correct location errors, achieving high accuracy without manual verification
3Productivity
If reference data with inaccurate initial locations is used, then the processing speed is maintained, but the final location accuracy deteriorates
Solution Approach 1:
The patent performs preliminary processing by generating multiple image channels (original, absolute difference, relative difference) from the reference data before the main neural network processing. This preliminary action prepares the data in a format that enables the network to quickly identify and correct location inaccuracies during processing, maintaining high speed while improving accuracy through pre-organized spatial relationships
Solution Approach 2:
The patent adds dimensional information by transforming 1D coordinate data into 2D image representations with multiple channels. This dimensional transformation allows the neural network to process location accuracy in a visual spatial context, enabling it to detect and correct inaccuracies in the reference data while maintaining processing efficiency through parallel channel analysis
Data Source
AI summary
Aspects of the invention include generating a combined raster image from point cloud data and reference data describing an original location of a power line. Selecting a set of candidate pixels from the combined raster image describing an updated location of a power line, wherein the selection is based at least in part on a location of pixels in the combined raster image that describe the original location. Detecting pixels from the set of candidate pixels that describe an updated location of a power line. Modifying the combined raster image to reflect the updated location of the power line.