Automated Geolocation via Targetable 3D Data Sets
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Solution Overview
Problem
Current methods for deriving targeting coordinates in image targeting applications rely on human selection of conjugate points, which is inefficient and lacks automation, requiring manual intervention for digital point positioning in stereo imagery.
Innovation Solution
The system uses a targetable 3D point set and registered 2D input images to determine geocoordinates, incorporating metadata for error computation and intersection algorithms to automate the geolocation process, allowing for accurate alignment of image pixels with 3D data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human selection of conjugate points is used for deriving targeting coordinates, then manual control and flexibility are maintained, but the process is inefficient and time-consuming
Solution Approach 1:
The system performs self-service by automatically identifying conjugate points and deriving targeting coordinates without human intervention. The automated algorithm processes the image data and 3D model to generate geolocation information independently, eliminating the need for manual point selection while maintaining accuracy through computational methods
Solution Approach 2:
The manual mechanical process of human point selection is replaced with an automated computational system. The algorithm substitutes human visual inspection and manual coordinate input with automated image processing, 3D model matching, and computational geometry to derive targeting coordinates efficiently
2Productivity
If automated algorithms are implemented for geolocation, then processing speed increases, but system complexity increases
Solution Approach 1:
The system achieves universality by creating a multi-functional automated platform that handles multiple tasks: image processing, 3D model registration, conjugate point identification, and coordinate derivation. This integrated system reduces overall complexity by consolidating multiple functions into a single cohesive algorithm rather than requiring separate systems for each task
Solution Approach 2:
The system performs preliminary actions by pre-processing images and pre-registering 3D models before the actual geolocation task. This preparation work includes organizing data structures, establishing coordinate systems, and pre-computing transformation matrices, which simplifies the main processing algorithm and reduces real-time computational complexity
3Measurement precision
If manual point selection is used, then accuracy depends on human expertise, but the process lacks consistency
Solution Approach 1:
The system implements feedback mechanisms by continuously comparing the derived geolocation results with the 3D model data and adjusting the algorithm parameters accordingly. This iterative process ensures that the automated system maintains high accuracy and consistency by learning from the data and correcting any deviations through computational feedback loops
Data Source
AI summary
A method can include identifying a geolocation of an object in an image, the method comprising receiving data indicating a pixel coordinate of the image selected by a user, identifying a data point in a targetable three-dimensional (3D) data set corresponding to the selected pixel coordinate, and providing a 3D location of the identified data point.