Raster Vector Aligner for GIS Data Registration
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Solution Overview
Problem
Geographic Information Systems (GIS) face challenges in aligning misaligned raster and vector data due to factors like obsolete data, projection errors, and different data vendors, making manual correction processes impractical with the increasing volume of data, especially with advanced applications like Google Earth.
Innovation Solution
A computer-implemented raster/vector aligner that generates edge and smoothness signals from raster data to create a combined signal for translation vector calculation, subdivides data into tiles for localized alignment, and computes global translation information to align raster data with vector data, using edge and roughness filtering and minimum-square best-match equations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual alignment correction processes are used, then alignment accuracy can be achieved, but the process becomes impractical with large data volumes
Solution Approach 1:
The system performs automatic self-alignment by computing translation vectors through signal processing and pattern matching algorithms. The raster data is automatically filtered, transformed, and compared with vector data to generate alignment corrections without requiring manual specification of control points, enabling the system to service itself.
Solution Approach 2:
The manual mechanical process of specifying control points and visually assessing alignment is replaced with automated signal processing operations. Edge filtering, smoothness filtering, and minimum-square best-match equations substitute for manual measurement and adjustment techniques.
2Measurement precision
If traditional conflation with control point pairs is used, then alignment can be achieved, but it requires significant manual intervention
Solution Approach 1:
The system automatically generates translation vectors through signal processing without requiring manual specification of control point pairs. The algorithm independently identifies features, computes signals, and determines alignment corrections, making the system self-sufficient.
Solution Approach 2:
The method extracts key features and signals from raster data through edge and smoothness filtering, separating the essential alignment information from the bulk data. This extraction eliminates the need for manual control point selection by automatically identifying and utilizing prominent features.
3Measurement precision
If data is processed as a whole, then global alignment can be achieved, but processing time increases significantly
Solution Approach 1:
The raster data is divided into directional chunks and processed in parallel using multiple filters operating simultaneously. This segmentation allows the system to process different portions of the data concurrently, reducing overall processing time while maintaining global alignment accuracy through integration of results.
Solution Approach 2:
Edge filtering and smoothness filtering are performed as preliminary operations to pre-process the raster data before the main alignment computation. This preliminary action prepares the data in advance, making the subsequent translation vector computation more efficient and accurate.
Data Source
AI summary
The present invention relates to methods for aligning raster and vector data.In an embodiment, a raster/vector aligner receives raster data and an approximate vector of a feature within the raster data. The raster/vector aligner generates an edge signal by edge filtering the raster data along a direction of the approximate vector and a smoothness signal by smoothness filtering the raster data along a direction of the approximate vector. The raster/vector aligner combines the edge signal and the smoothness signal into a combined signal which is used to generate a translation vector or a signal weight for the feature within the raster data.