Structure Extent Change Detection Using Image Alignment Confidence
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Solution Overview
Problem
Current methods for detecting changes in the extent of structures from digital images are computationally expensive and prone to errors, whether manual or automated, lacking accuracy and efficiency in identifying structural changes.
Innovation Solution
A system utilizing a convoluted neural network or generative adversarial network to align a structure shape with pixels in an aerial image, generating an alignment confidence score, and determining changes based on a predetermined threshold, with edge detection and shift adjustments to enhance alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual comparison methods are used to detect changes in structure extent, then measurement precision can be achieved through human perception, but productivity is severely reduced due to time-consuming manual tracing and comparison
Solution Approach 1:
The patent replaces manual mechanical tracing and visual comparison with an automated computer-based system that uses image processing algorithms to detect and measure structural changes. The system automatically traces structure boundaries and compares extents between images, eliminating the need for manual human intervention while maintaining measurement precision through computational analysis.
Solution Approach 2:
The system enables self-service by allowing the computer to autonomously perform the entire change detection process without human assistance. The automated algorithm independently traces structures, compares extents, identifies changes, and generates measurements, making the process self-sufficient and dramatically improving productivity while preserving accuracy through systematic computational methods.
2Productivity
If algorithmic tracing methods are used to detect changes in structure extent, then productivity is improved through automated processing, but measurement precision deteriorates due to errors in extracting extent and computational complexity
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously refines its tracing algorithms based on comparison results and error detection. The automated process uses feedback loops to adjust extent extraction parameters, validate trace accuracy, and correct errors, thereby improving measurement precision while maintaining high productivity through systematic self-correction.
Solution Approach 2:
The system performs preliminary actions by pre-processing images and pre-defining structure characteristics before the actual comparison process. This preliminary preparation includes identifying structure types, setting expected boundary characteristics, and pre-calibrating measurement parameters, which ensures higher accuracy in extent extraction while maintaining efficient automated processing.
3Productivity
If simple pixel analysis is used to detect structural changes, then computational complexity is reduced and processing speed is improved, but measurement precision is insufficient due to inability to accurately identify structure boundaries
Solution Approach 1:
The patent applies segmentation by dividing the image analysis process into distinct stages: initial pixel-level screening to identify potential structure locations, followed by boundary tracing to define precise extents, and finally change detection to measure differences. This segmented approach maintains computational efficiency through hierarchical processing while achieving high boundary detection accuracy through specialized algorithms at each stage.
Solution Approach 2:
The system transitions from simple two-dimensional pixel analysis to multi-dimensional processing by incorporating boundary coordinate information, extent area calculations, and spatial relationship analysis. This dimensional enhancement allows the system to maintain processing efficiency while significantly improving boundary detection accuracy through richer data representation and more sophisticated comparison metrics.
Data Source
AI summary
Systems and methods for automated detection of changes in extent of structures using imagery are disclosed, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: align an outline of a structure at a first instance of time to pixels within an image depicting the structure, the image captured at a second instance of time; assess a degree of alignment between the outline and the pixels within the image depicting the structure, using a machine learning model to generate an alignment confidence score; determine an existence of a change in extent of the structure based upon the alignment confidence score indicating that the outline and the pixels within the image are not aligned; identify a shape of the change in extent of the structure; and store the shape of the change in extent of the structure.