Road Line Extraction Using Image Segmentation and Trajectory Similarity
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Solution Overview
Problem
Current methods for acquiring road data, such as using acquisition vehicles, user trajectories, and satellite imagery, face limitations like high costs, low timeliness, inaccuracies due to environmental factors, and difficulties in identifying missing roads in dense or sparse road networks.
Innovation Solution
A method that segments road lines using a segmentation mask from images and trajectory information to determine target road lines by calculating similarities between sub-road lines, integrating image and trajectory data for accurate road line extraction and updating maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acquisition vehicles are used to acquire road data, then road data can be collected, but the cost is high and timeliness is low
Solution Approach 1:
The patent uses user trajectory data as a copy or alternative source to acquire road data, replacing the need for expensive acquisition vehicles. User trajectories are processed and cleaned to extract road information, achieving both cost reduction and improved timeliness while maintaining data accuracy through multiple data source integration.
Solution Approach 2:
The system integrates multiple data sources (user trajectories, satellite imagery, segmentation masks) into a unified road data acquisition framework. This multi-functional approach allows the system to leverage different data sources for different scenarios, improving both timeliness and accuracy simultaneously.
2Productivity
If satellite imagery is used to acquire road data, then road data can be obtained, but accuracy is reduced due to environmental factors
Solution Approach 1:
The patent merges satellite imagery with segmentation masks and user trajectory data to compensate for the limitations of satellite imagery alone. By combining multiple data sources, the system maintains high productivity from satellite imagery while improving accuracy through the complementary information from other sources.
Solution Approach 2:
The system uses segmentation masks derived from satellite imagery to guide and validate the extraction of road data from user trajectories. This feedback mechanism ensures that the final road data is both efficient to acquire and accurate, as the segmentation masks provide a reference framework for validating trajectory-based extractions.
3Ease of manufacture
If traditional methods are used to identify missing roads, then road data processing can be performed, but it is difficult to identify missing roads in dense or sparse road networks
Solution Approach 1:
The patent segments user trajectories into multiple trajectory lines and processes them individually to identify missing roads. This segmentation approach allows the system to handle both dense and sparse road networks effectively by breaking down complex trajectory data into manageable segments that can be analyzed for gaps and missing road connections.
Data Source
AI summary
A method for processing a map, an electronic device, and a storage medium, which relate to a technical field of computer technology, in particular to computer vision technology and high-definition map technology. The method includes: segmenting a first road line to obtain a plurality of first sub-road lines, wherein the first road line is obtained according to a segmentation mask for an image, and the image corresponds to a target region; segmenting a second road line to obtain a plurality of second sub-road lines, wherein the second road line is obtained according to a trajectory information corresponding to the target region; and determining a target road line according to first similarities between the plurality of first sub-road lines and the plurality of second sub-road lines.