Mosaic Seamline Generation Using Semantic Segmentation to Avoid Buildings
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Solution Overview
Problem
Current methods for generating geospatial mosaics often result in seamlines passing through important structures like buildings due to a lack of semantic understanding, leading to unnatural-looking mosaics and requiring time-consuming manual intervention.
Innovation Solution
A machine learning-based approach that combines pixel-level similarity with semantic segmentation to generate seamlines that avoid structures by using a Convolutional Neural Network (CNN) to predict structure probabilities and integrate these with distance-based cost functions, ensuring seamlines do not cross buildings or other structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional seamline generation methods are used, then the processing speed is fast, but the seamlines pass through structures leading to poor visual quality
Solution Approach 1:
The system performs preliminary semantic segmentation to identify structures before seamline generation. By pre-labeling pixels as structure or non-structure and pre-computing cost matrices incorporating structure avoidance, the system prepares optimization-ready data that enables accurate structure-avoiding seamlines to be generated quickly without manual intervention.
2Manufacturing precision
If manual intervention is used to avoid structures in seamlines, then the visual quality is improved, but the processing time increases significantly
Solution Approach 1:
The system performs self-service by automatically identifying structures through semantic segmentation and autonomously generating optimized seamlines that avoid these structures. The machine learning model self-adjusts the cost matrix and seamline optimization parameters without human input, eliminating the need for manual intervention while maintaining high seamline quality and processing efficiency.
3Measurement precision
If semantic segmentation is applied to identify structures, then structure detection accuracy is improved, but the computational complexity increases
Solution Approach 1:
The system extracts only the essential semantic information needed for seamline optimization - specifically, binary structure labels for each pixel. By extracting only this critical information rather than full semantic annotations, the system achieves sufficient structure detection accuracy for seamline avoidance while keeping computational complexity manageable and integration with existing seamline algorithms straightforward.
Data Source
AI summary
Methods and systems for avoiding buildings and other structures using semantic segmentation, the method including obtaining a plurality of image strips of a geographic location, identifying one or more structures that are present in one or more image strips, and generating one or more seamlines between adjacent image strips using a machine learning model. When two or more adjacent image strips include one or more structures, the method includes circumventing the one or more structures with the generated seamlines therebetween, wherein the generated one or more seamlines form a visual connection between the adjacent image strips.