AI Geospatial Model Update via Iterative Image Registration
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Solution Overview
Problem
Current geospatial modeling systems face challenges in efficiently generating accurate 3D models from multiple sources of geospatial images, particularly in handling large-scale satellite data of inhomogeneous quality and resolution, and require manual parameter-tuning processes, which are time-consuming and labor-intensive.
Innovation Solution
A geospatial modeling system utilizing Artificial Intelligence (AI) to generate and update 3D geospatial models based on a plurality of geospatial images, including selecting isolated images, determining reference images, aligning them, and iteratively repeating the process until alignment reaches a threshold, while simulating atmospheric phenomena and using multi-modal image registration techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional geospatial modeling systems process multiple sources of geospatial images manually, then accuracy can be maintained through careful parameter tuning, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical parameter-tuning processes with an automated AI-based system. The AI algorithm automatically selects optimal parameters for image registration and 3D model generation, eliminating the need for manual intervention while maintaining or improving accuracy. This substitution of human operator mechanics with intelligent automation directly resolves the contradiction between accuracy and time consumption.
Solution Approach 2:
The system implements self-service through automated workflows where the AI algorithm independently performs image selection, parameter optimization, registration, and model generation without external human input. The system serves itself by automatically adjusting parameters based on the characteristics of the input images, thereby eliminating labor-intensive manual processes while preserving accuracy through intelligent decision-making.
2Measurement precision
If manual parameter-tuning is used to handle inhomogeneous quality and resolution of satellite data, then processing accuracy can be maintained, but the process becomes labor-intensive
Solution Approach 1:
The patent replaces manual parameter-tuning mechanics with an AI-based automated system that intelligently handles inhomogeneous satellite data. The AI algorithm automatically analyzes image quality and resolution characteristics, selecting appropriate registration parameters without human intervention. This substitution maintains accuracy while dramatically reducing operational complexity by eliminating manual tuning requirements.
Solution Approach 2:
The system dynamically changes parameters based on the specific characteristics of each input image. The AI algorithm automatically adjusts registration parameters, scale factors, and transformation models according to the inhomogeneous quality and resolution of different satellite images. This adaptive parameter changing enables accurate processing of diverse data sources without requiring manual intervention for each image set.
3Productivity
If traditional image processing methods are used for aligning multiple geospatial images, then processing can be completed, but the speed and efficiency are insufficient for high-resolution satellite imagery
Solution Approach 1:
The patent replaces traditional mechanical image alignment methods with an AI-based automated registration system. The AI algorithm efficiently processes high-resolution satellite imagery by automatically identifying corresponding features, calculating transformation parameters, and performing precise alignment. This substitution dramatically increases processing speed while maintaining or improving alignment accuracy through intelligent feature recognition and robust mathematical modeling.
Solution Approach 2:
The system performs preliminary actions by pre-processing images to enhance features, pre-identifying key correspondence points, and pre-calculating optimal registration parameters before the actual alignment process. This preliminary preparation enables faster subsequent processing while ensuring high alignment accuracy, directly addressing the contradiction between speed and precision in handling high-resolution imagery.
4Productivity
If automated workflows are implemented for 3D model generation, then efficiency and speed improve, but the system requires advanced AI algorithms and increased complexity
Solution Approach 1:
The patent replaces complex manual workflow coordination with a unified AI-based automated system. The AI algorithm integrates multiple processing stages (image selection, registration, parameter optimization, and 3D reconstruction) into a cohesive automated workflow. While the underlying AI technology is advanced, the system presents a simplified interface and requires no manual intervention, thereby improving efficiency while managing complexity through intelligent integration rather than human coordination.
Data Source
AI summary
A geospatial modeling system may include a memory and a processor cooperating therewith to: (a) generate a three-dimensional (3D) geospatial model including geospatial voxels based upon a plurality of geospatial images; (b) select an isolated geospatial image from among the plurality of geospatial images; (c) determine a reference geospatial image from the 3D geospatial model using Artificial Intelligence (AI) and based upon the isolated geospatial image; (d) align the isolated geospatial image and the reference geospatial image to generate a predictively registered image; (e) update the 3D geospatial model based upon the predictively registered image; and (f) iteratively repeat (b)-(e) for successive isolated geospatial images.