Smart Map Generation Using AI Element Extraction
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Solution Overview
Problem
Existing map generation technologies face challenges in efficiently extracting and utilizing various element data from unstructured maps, leading to cumbersome and time-consuming processes, especially when dealing with complex or large-scale maps.
Innovation Solution
A method and system for generating smart maps by automatically extracting and subtracting multiple types of element data from unstructured maps, using image processing technologies and pattern recognition algorithms to recognize and extract contours and other element data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual extraction of element data is performed on unstructured maps, then extraction accuracy can be maintained, but work time and costs increase significantly
Solution Approach 1:
The patent segments the map extraction process into multiple passes, where different AI models extract different types of element data (e.g., parking surfaces, roads, buildings) in sequence. Each pass focuses on specific element types, improving both accuracy and efficiency compared to manual extraction of all elements.
Solution Approach 2:
The patent replaces manual mechanical extraction with automated AI-based image processing systems. Multiple specialized AI models automatically identify and extract element data from unstructured maps, eliminating the need for manual intervention while maintaining high extraction accuracy.
2Loss of time
If AI models are used to automatically extract element data from unstructured maps, then work time is reduced, but extraction accuracy decreases due to various drawing styles
Solution Approach 1:
The patent divides the extraction task into multiple specialized AI models, each trained to recognize specific element types (parking surfaces, roads, buildings, etc.). This segmentation allows each model to focus on particular drawing styles and patterns, improving overall extraction accuracy while maintaining automation efficiency.
Solution Approach 2:
The patent adjusts extraction parameters dynamically based on the detected element type and drawing style. Different AI models use different parameter settings optimized for their specific element types, allowing accurate extraction despite variations in drawing styles across different maps.
3Quantity of substance
If element data is extracted from large-scale complex maps, then comprehensive data coverage is achieved, but manual extraction becomes infeasible
Solution Approach 1:
The patent processes large-scale maps by dividing them into smaller regions and processing each region with appropriate AI models. This segmentation makes it feasible to extract comprehensive data from large maps that would be impossible to handle manually, while maintaining data quality through systematic processing.
Solution Approach 2:
The patent employs a universal extraction system that can handle various map types, scales, and complexities using the same automated AI-based approach. This multi-functional system makes comprehensive data extraction feasible across different map sizes and complexities without requiring manual intervention.
4Quantity of substance
If multiple element data types are extracted from unstructured maps, then data completeness improves, but process complexity increases
Solution Approach 1:
The patent manages process complexity by segmenting the extraction of different element data types into separate, specialized AI models. Each model handles specific element types independently, making the overall complex process manageable and maintainable while achieving complete data extraction across multiple element categories.
Data Source
AI summary
Provided is a method for generating a smart map. The method includes: receiving a pre-prepared unstructured map; extracting and subtracting at least one target element data among a plurality of element data included in the unstructured map; and constructing a smart map based on the extracted element data when the extracting and subtracting of the target element data from the unstructured map is completed.