Pixel Categorization Model for High-Resolution LULC Mapping
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Solution Overview
Problem
Existing methods for categorizing image pixels in land use/land cover (LULC) maps are inadequate for non-geospatial experts, lacking an efficient and high-level automated geospatial analysis solution to convert abundant pixel data into actionable insights.
Innovation Solution
A machine learning model is trained to categorize each pixel of an overhead image using a plurality of images, incorporating boundary information and spatial and spectral features, with biasing for under-represented categories, and generating maps at high resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional pixel classification methods are used, then the process is simple and fast, but the output is not actionable for non-geospatial experts
Solution Approach 1:
The patent introduces an automated geospatial analysis system as an intermediary between raw pixel data and non-expert users. This system performs complex categorization, boundary detection, and pattern recognition tasks automatically, translating raw image data into actionable insights without requiring expert intervention while maintaining high information retention
2Measurement precision
If high-resolution maps are generated, then the detail and accuracy improve, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on extensive geospatial data before actual map generation. This pre-processing creates reusable knowledge structures that enable rapid high-resolution map generation without requiring extensive computational resources during the actual processing phase
Solution Approach 2:
The patent replaces traditional mechanical pixel-by-pixel classification methods with machine learning-based automated analysis. This substitution enables parallel processing of multiple pixels simultaneously, dramatically reducing processing time while maintaining or improving resolution through pattern recognition across the entire image
3Productivity
If automated analysis is implemented, then the processing speed increases, but the accuracy and reliability may decrease
Solution Approach 1:
The system implements feedback mechanisms where the automated analysis results are continuously refined based on performance metrics and validation against known geospatial patterns. This feedback loop maintains high accuracy by identifying and correcting errors while preserving the speed benefits of automation through optimized processing pathways
4Measurement precision
If boundary information is incorporated, then the categorization accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent merges boundary detection with the overall pixel categorization process rather than treating them as separate operations. By combining these functions into a unified machine learning model, the system achieves high boundary detection accuracy while avoiding the complexity of multiple separate processing systems
Data Source
AI summary
Systems and methods are described to systems and methods for training a machine learning model to categorize each pixel of an input overhead image using received overhead images, and using a trained machine learning model to determine, for each pixel of input overhead images, to which land use or land cover mapping category each pixel of each overhead image belongs. The provided systems and methods may generate a map of a geographic area associated with the plurality of overhead images based on the plurality of overhead images and on the determined categories.