Geospatial Modeling System with User-Selectable Building Shapes
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Solution Overview
Problem
Existing geospatial modeling systems face challenges in efficiently detecting and correcting errors, particularly in 3D topographical models, due to data voids and manual editing requirements, which are time-consuming and prone to user error.
Innovation Solution
A geospatial modeling system that includes a processor for identifying localized error regions, calculating error values, and inpainting or replacing building data points with user-selectable shapes to correct errors, allowing for automated error detection and correction with enhanced feature rendering options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual editing is used to correct errors in geospatial models, then customization and accuracy can be achieved, but time consumption and user error increase
Solution Approach 1:
The system performs preliminary automated error detection and correction by identifying candidate correction regions and generating correction options before user interaction. This preliminary action reduces the time required for manual editing while maintaining accuracy, as the system has already prepared multiple correction options for user selection.
Solution Approach 2:
The system creates copies of existing building data and correction options to provide multiple alternative corrections. By generating multiple candidate correction regions and options, the system allows users to select the most appropriate correction without performing multiple manual editing iterations, thus reducing time loss.
2Productivity
If automated error detection and correction is implemented, then time efficiency improves, but system complexity increases
Solution Approach 1:
The system segments the error correction process into distinct automated stages: error detection, candidate region identification, correction option generation, and user presentation. This segmentation enables automated processing to handle routine tasks efficiently while keeping the system complexity manageable through modular design.
Solution Approach 2:
The system introduces an intermediary layer that bridges automated error detection and user decision-making. This intermediary generates and presents multiple correction options to users, allowing automated processing to improve productivity while the user maintains control over final corrections, balancing system complexity with effectiveness.
3Adaptability or versatility
If multiple building shape options are provided, then user selectability and customization improve, but data processing complexity increases
Solution Approach 1:
The system applies local quality by generating different building shape options tailored to specific candidate correction regions based on their individual characteristics. Each region receives customized correction options appropriate to its local context, improving adaptability while processing complexity is managed through region-specific rather than global processing.
Solution Approach 2:
The system generates multiple building shape options (excessive action) for each candidate correction region to provide user selectability. This partial generation of options—creating several variants rather than a single correction—improves adaptability while the options are generated on-demand for specific regions rather than for all data simultaneously.
4Reliability
If error detection and correction features are added to existing systems, then model quality improves, but existing system functionality may be disrupted
Solution Approach 1:
The system merges error detection, candidate region identification, and correction option generation into an integrated workflow that works alongside existing geospatial modeling functionality. This merging improves model quality through comprehensive error handling while maintaining compatibility with existing system functions through unified processing.
Solution Approach 2:
The system implements multi-functional components that can operate in different modes: automated error detection, candidate region identification, and correction option generation. These universal components can be applied to various types of geospatial data and work with existing modeling workflows, improving model quality without requiring separate specialized systems.
Data Source
AI summary
A geospatial modeling system may include a geospatial model data storage device, a user input device, and a display. A processor may be included for cooperating with the geospatial model data storage device, the user input device and the display for displaying a geospatial model data set on the display including at least one group of building data points, and displaying a plurality of user-selectable different building shapes on the display based upon the at least one group of building data points. The plurality of user-selectable different building shapes may have different respective feature detail levels. The processor may further replace the at least one group of building data points with a given one of the user-selectable different building shapes based upon user selection thereof with the user input device.