Hierarchical Topographic Feature Classification via Convolutional Neural Networks
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Solution Overview
Problem
Current methods for topographic data analysis in the field of machine learning are limited to single-level classification, failing to leverage hierarchical classification techniques, which are prevalent in other image processing domains, thereby limiting the accuracy and detail of topographic and cartographic representations.
Innovation Solution
The application of machine learning techniques, specifically convolutional neural networks, to process topographic imagery for hierarchical classification, enabling the recognition and semantic labeling of topographic features within a semantic topographic hierarchy, allowing for more detailed and accurate representations of geographic areas, including change detection, geographic transfer, discovery, and inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single-level classification is used for topographic data analysis, then the classification process is simple, but the accuracy and detail of topographic representations deteriorate
Solution Approach 1:
The patent applies segmentation by dividing the classification process into multiple hierarchical levels. Instead of a single-level classification, the system implements a hierarchical structure where classification proceeds through multiple stages, each refining the categorization of topographic features. This segmentation allows the system to maintain manageable complexity at each level while achieving high overall accuracy in topographic representation.
Solution Approach 2:
The patent introduces a hierarchical dimension to the classification process. By adding the level hierarchy as an additional dimension beyond simple category classification, the system transforms a flat single-level approach into a multi-dimensional hierarchical structure. This enables more nuanced and accurate topographic feature classification without proportionally increasing operational complexity.
2Measurement precision
If hierarchical classification is implemented, then the accuracy and detail of topographic representations improve, but the device complexity increases
Solution Approach 1:
The hierarchical classification system is segmented into distinct levels and stages, where each level handles specific aspects of topographic feature classification. This segmentation breaks down the complex hierarchical task into manageable sub-tasks, reducing the perceived and actual complexity of implementing and operating the system while maintaining high classification accuracy.
Solution Approach 2:
The patent implements dynamic characteristics in the hierarchical classification system, allowing the classification process to adapt and progress through different levels based on the complexity and specificity of the topographic features being classified. This dynamic approach enables the system to optimize its operational complexity by only engaging deeper hierarchical levels when necessary, rather than applying maximum complexity uniformly to all classification tasks.
Data Source
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AI summary
Embodiments of the invention apply machine learning techniques for image recognition and classification to the processing of topographic imagery, in order to permit more accurate and detailed topographic representations of an area to be obtained. In particular, in one embodiment a machine learning system is trained with existing topographic imagery and corresponding topographic data relating to a particular area, so that the machine learning system is then able to relate actual physical topographical features to their topographic representations in existing data. Having been so trained, the machine learning system may then be used to process topographic imagery data of the same area to determine new topographic details thereof for incorporation into the topographic data.