Spatial Data Classification via Feature Segmentation

The patent segments the spatial point cloud data processing into distinct stages: unsupervised learning for initial feature extraction and grouping, followed by supervised learning for classification. This segmentation allows the system to first identify patterns without requiring labeled data, then apply classification only to the extracted features, significantly reducing the training data burden while maintaining high automation.

The patent implements self-service through automated feature extraction and grouping using unsupervised learning algorithms. The system automatically identifies patterns, clusters points into meaningful groups, and extracts features without human intervention. This self-service capability maintains high accuracy by leveraging algorithmic pattern recognition while eliminating the time-consuming manual labeling process.

The patent applies preliminary action by using unsupervised learning to pre-process and extract features from the point cloud data before classification. The system performs preliminary grouping and feature extraction that identifies patterns and structures in the data, creating a refined dataset that is then easier to classify. This preliminary processing step makes the system adaptable to rare objects and diverse environments without requiring billions of training examples.

The patent implements feedback mechanisms where the system continuously refines its classification based on the extracted features and learned patterns. The unsupervised learning component provides feedback on data structure and patterns, which informs the supervised classification process. This internal feedback loop ensures high reliability by automatically correcting errors and inconsistencies without requiring external manual quality analysis.