Machine-Assisted Map Editing with Neural Network Vectorization
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Solution Overview
Problem
Traditional digital map creation tools are time-consuming and prone to human error, requiring manual tracing of architectural drawings and often necessitating training in CAD or image editing software, making it difficult for untrained users to create accurate and efficient digital maps of indoor spaces.
Innovation Solution
A machine-assisted map editing system using a trained neural network to classify architectural data, generate vectorized polygon representations, and assist users in tracing features, automatically placing polygon objects and connections, while allowing user input for modification and feedback to improve the model's accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional manual tracing methods are used, then users can create digital maps with full control, but the process is time-consuming and requires training in CAD or image editing software
Solution Approach 1:
The patent replaces manual mechanical tracing operations with an automated machine learning system. The neural network automatically processes architectural drawings and generates vectorized polygon representations, substituting the mechanical action of manual tracing with an automated computational process that requires no specialized training
Solution Approach 2:
The system performs self-service by automatically classifying architectural features and generating map elements without human intervention. The neural network independently processes the base map, identifies walls, doors, windows, and other features, and creates the editable map structure autonomously
2Reliability
If manual tracing by untrained users is performed, then no specialized software training is needed, but human error increases and accuracy decreases
Solution Approach 1:
The patent replaces error-prone manual tracing with an automated neural network system that consistently identifies architectural features. The machine learning model processes the base map objectively without human error, improving reliability while keeping the user interface simple
Solution Approach 2:
The neural network acts as an intermediary between the base map and the final editable map. It processes the architectural drawing, classifies features, and generates accurate vectorized representations, serving as a reliable mediator that eliminates human error in the tracing process
3Productivity
If automated machine learning classification is used, then map creation speed increases, but the system requires trained neural networks and processing complexity
Solution Approach 1:
The neural network is trained in advance on large datasets of architectural drawings to learn feature classification. This preliminary training action enables the system to rapidly process new base maps without requiring complex real-time processing, achieving high productivity through pre-computed knowledge
Solution Approach 2:
The trained neural network serves as an intermediary processing layer that automatically transforms base maps into editable maps. It handles the complexity of feature classification and vectorization internally, presenting a simple interface to users while maintaining high processing speed
Data Source
AI summary
System and methods for machine-assisted map editing tools are described. The system includes a machine-learning neural network trained to classify architectural data in a base map and generate a vectorized representation of the architectural data. The base map is presented to a user on a display interface for receiving user input tracing architectural features in the base map. The system then proposes or automatically places polygon objects representing the traced features in an editable map. The editable map may be superimposed on the base map on the display interface as it is drawn. The system may further detect the position of connections, classify enclosed spaces as room types. The machine-assisted map editing tools described herein provide a fast and efficient way for untrained users with no image/map editing experience to trace an architectural base map to generate an editable map of architectural features.