GIS Data Gap Filling via Pixelization for Infrastructure Optimization
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Solution Overview
Problem
Inaccurate and incomplete GIS data hinders the effective deployment of linear infrastructure projects, such as hyperloop systems, as AI models require comprehensive and precise data to optimize infrastructure configurations, leading to stalled deployments and environmental degradation.
Innovation Solution
The method involves receiving a rasterized cost map with an initial alignment curve, determining pixelization factoring data, and generating a pixelized cost map to fill data gaps, thereby creating a more comprehensive and accurate representation of costs for AI optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If AI models are used to optimize infrastructure configurations, then infrastructure deployment efficiency is improved, but the requirement for complete and accurate GIS data increases, which worsens the data availability situation
Solution Approach 1:
The system performs preliminary gap filling of GIS data before AI optimization by identifying missing data regions and synthesizing plausible values using interpolation algorithms. This preliminary action ensures that complete GIS data is available upfront, enabling AI models to function effectively without waiting for additional data collection
Solution Approach 2:
The patent introduces an intermediary data processing layer between incomplete GIS data and AI models. This intermediary system includes modules for detecting data gaps, selecting appropriate filling algorithms, and synthesizing missing information. It acts as a mediator that transforms incomplete data into complete datasets suitable for AI optimization
2Ease of manufacture
If traditional GIS data processing methods are used, then data processing simplicity is maintained, but infrastructure optimization accuracy deteriorates due to incomplete data
Solution Approach 1:
The data processing system is segmented into distinct functional modules: a gap detection module that identifies missing data regions, a algorithm selection module that chooses appropriate filling methods, and a data synthesis module that generates missing values. This segmentation maintains processing simplicity through modular design while achieving high accuracy through specialized functions
Solution Approach 2:
The system dynamically changes processing parameters based on data characteristics. When gaps are detected, the system adjusts the data completeness parameter and selects appropriate interpolation algorithms (e.g., kriging, inverse distance weighting) based on the spatial distribution and type of missing data, thereby maintaining simplicity while improving accuracy
3Loss of time
If GIS data gaps are not filled, then processing time is reduced, but infrastructure deployment reliability deteriorates
Solution Approach 1:
The gap filling system operates autonomously without requiring external intervention. The system automatically detects data gaps, selects appropriate filling algorithms, and synthesizes missing information using built-in computational resources. This self-service capability ensures reliable infrastructure deployment decisions are made quickly without manual data collection or external data sources
Data Source
AI summary
A method for gap filling of geographic information service (“GIS”) data includes receiving a rasterized cost map including a first alignment curve between two locations. The method also includes determining a pixelization factoring data comprising a pixelization factoring value and pixelization factoring metadata. The method further includes generating a pixelized cost map based on the pixelization factoring data and the rasterized cost map. The method still further includes generating a second alignment curve between the two points in accordance with the pixelized cost map.