Geo-Referenced Map Creation with AI Grid Intersection Detection
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Solution Overview
Problem
The manual process of identifying ground control points (GCPs) on visual flight rule (VFR) charts is labor-intensive, subjective, and error-prone, leading to inconsistencies and inaccuracies in geo-referencing maps.
Innovation Solution
A method and system that automatically determine the locations of actual lines of longitude and latitude, and their intersections, using AI or machine-learning systems to analyze vast amounts of data and identify actual intersections, thereby creating geo-referenced maps with real-world coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual review of VFR charts is used to locate ground control points, then flexibility in handling different chart types is maintained, but labor intensity increases and productivity decreases
Solution Approach 1:
The patent replaces the manual mechanical process of reviewing and marking ground control points with an automated image processing system. The system automatically detects chart features, identifies ground control point locations, and performs geo-referencing operations without human intervention, thereby increasing productivity while maintaining adaptability through configurable detection algorithms.
Solution Approach 2:
The system performs self-service by automatically processing VFR charts, detecting features, and generating geo-referenced maps without requiring operator intervention. The automated algorithm independently completes the entire geo-referencing workflow, eliminating the need for manual review while maintaining accuracy through built-in validation mechanisms.
2Adaptability or versatility
If manual selection of ground control points is performed, then operator judgment can be applied to difficult cases, but measurement precision decreases due to subjectivity and inconsistencies
Solution Approach 1:
The system incorporates feedback mechanisms where detected ground control points are validated against multiple reference sources and previous detections. The algorithm iteratively refines its measurements by comparing detected features against known geographic coordinates and adjusting its identification criteria, thereby achieving high measurement precision while handling difficult cases through learned patterns from feedback loops.
Solution Approach 2:
The system changes detection parameters dynamically based on chart complexity and difficulty level. When encountering difficult cases, the algorithm automatically adjusts its feature detection sensitivity, zoom level, and matching criteria to optimize precision. This parameter adaptation allows the system to maintain high measurement precision across varying chart types and difficulties without requiring operator intervention.
3Adaptability or versatility
If manual marking of ground control points is performed, then flexibility in handling edge cases is maintained, but error rate increases due to human fatigue and subjectivity
Solution Approach 1:
The patent replaces manual marking operations with automated image processing that systematically identifies ground control points using consistent algorithms. The system processes every feature uniformly through programmed criteria, eliminating human fatigue and subjectivity that lead to errors. Edge cases are handled through configurable detection rules that can be adjusted without human intervention, maintaining flexibility while reducing error rates.
4Productivity
If automated detection of ground control points is implemented, then productivity increases and consistency improves, but device complexity increases
Solution Approach 1:
The system segments the complex geo-referencing task into distinct functional modules: image processing, feature detection, ground control point identification, coordinate transformation, and map generation. Each module handles a specific sub-function independently, making the overall complex system manageable and maintainable. This segmentation allows high productivity through automation while keeping device complexity organized and controlled through modular architecture.
Data Source
AI summary
A system and a method include identifying characteristics of a map of a geographic location, and placing plural first lines of longitude and plural first lines of latitude of the map based in part on the characteristics. The first lines of longitude and latitude intersect with each other to create plural theoretical intersections. A magnification of a segment of the map, that includes the first theoretical intersection, is changed. One or more edges associated with one or more second lines of longitude or latitude are identified, and a location of a first actual intersection within the first segment is determined based in part on the identification of the one or more edges associated with the second lines of longitude and latitude.