Geospatial Void Filling via Self-Similarity Elevation Adjustment
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Solution Overview
Problem
Existing geospatial modeling systems face challenges in accurately filling voids in data sets, leading to reduced model quality due to edge artifacts and computational burdens, particularly in 3D topographical models.
Innovation Solution
A geospatial modeling system that determines voids within a data set, selects raw fill regions, adjusts elevation values based on differential surfaces, and updates the data set to minimize artifacts, allowing for both all-at-once and iterative filling methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional interpolation techniques (sinc, polynomial, spline) are used to fill voids, then void filling is achieved, but edge artifacts are introduced and computational overhead increases
Solution Approach 1:
The patent uses self-similarity within the data set itself to fill voids. The algorithm identifies regions with similar characteristics and uses them as sources to fill missing data, eliminating the need for external fill sources and avoiding edge artifacts caused by conventional interpolation techniques
Solution Approach 2:
The patent transforms the void filling problem by changing the approach from interpolation-based to similarity-based. Instead of using mathematical interpolation that introduces artifacts, it changes to using empirical similarity matching that preserves edge characteristics and reduces harmful artifacts
2Manufacturing precision
If conventional interpolation techniques are used to fill voids, then void filling is achieved, but computational overhead becomes burdensome
Solution Approach 1:
The patent applies partial action by using multiple raw fill regions iteratively to fill a single void region. This approach balances computational effort with accuracy, avoiding the excessive computational overhead of high-order polynomial methods while achieving desired model quality through successive approximations
Solution Approach 2:
The algorithm uses the data set itself as the source for filling voids, eliminating the need for external data sources and complex interpolation mathematics. This self-service approach reduces computational overhead by using simple similarity comparisons instead of burdening the system with high-order polynomial calculations
3Manufacturing precision
If external fill sources are used to fill voids, then void filling is achieved, but additional data requirements and processing complexity increase
Solution Approach 1:
The patent makes the system self-sufficient by using the existing data set itself as the source for filling voids. This eliminates dependency on external fill sources and simplifies the processing pipeline, while still achieving accurate void filling through self-similarity matching within the available data
Solution Approach 2:
The patent creates a universal solution that works with any data set containing self-similar regions. The method is adaptable to different types of geospatial data and void patterns, providing flexibility without requiring external specialized fill sources for different scenarios
Data Source
AI summary
A geospatial modeling system may include a geospatial model data storage device and a processor cooperating therewith for determining a void within a geospatial model data set defining a void boundary region, and selecting at least one raw fill region from within the geospatial model data set for filling the void. The processor may also cooperate with the geospatial model data storage device for adjusting elevation values of the at least one raw fill region based upon elevation differences between corresponding portions of the void boundary region and the at least one raw fill region, and updating the geospatial model based upon the adjusted elevation values of the at least one raw fill region.