Texture Symbol Generation for Land Use Maps
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Solution Overview
Problem
Current land use classification maps at large scales lack vivid and semantically rich representations, with static abstract symbols failing to convey detailed information and failing to meet the requirement of clear semantic meaning, especially in large-area maps where features are sparse, and existing texture methods do not effectively express land use classification.
Innovation Solution
A method for creating texture symbols using texture materials from satellite images, real-scene photos, and paintings, involving main color extraction, color clustering, texture skeleton extraction, and tile effect removal to form a texture library that can be used for map rendering, allowing for clear semantic and quantitative representation of land types and seasonal changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If static abstract symbols are used for land use classification, then the map rendering is simple and clear, but the semantic richness and detailed information representation are insufficient
Solution Approach 1:
The patent segments the texture symbol into multiple layers including background layer, primary texture layer, and secondary texture layer. Each layer serves a specific function: the background layer provides base color, the primary texture layer contains main texture features, and the secondary texture layer adds detailed patterns. This segmentation allows rich semantic information to be organized in a structured manner while maintaining rendering clarity.
Solution Approach 2:
The patent implements a nested structure where multiple texture layers are superimposed on each other. The background layer is at the base, with the primary texture layer nested above it, and the secondary texture layer nested at the top. This nesting arrangement allows different levels of detail to coexist, with inner layers providing additional semantic information without obscuring outer layers.
2Loss of information
If texture materials from multiple sources are used, then the semantic representation and visual quality are improved, but the processing complexity and time consumption increase
Solution Approach 1:
The patent performs preliminary processing of texture materials by extracting and pre-processing texture features before they are needed for map rendering. Texture materials from satellite images, real-scene photos, and paintings are pre-processed to extract key features and convert them into usable texture symbol formats. This preliminary action reduces processing time during actual map generation.
Solution Approach 2:
The patent creates copies of texture materials through extraction and synthesis processes. Instead of working directly with original complex texture data, the system extracts essential texture features and creates simplified copies that retain semantic information. These copies are then used for map rendering, significantly reducing processing requirements while maintaining information accuracy.
3Manufacturing precision
If detailed texture features are included, then the representation accuracy and semantic meaning are enhanced, but the map rendering complexity and processing requirements increase
Solution Approach 1:
The patent applies local quality by differentiating the level of detail in different texture layers. The background layer uses simple color information, the primary texture layer includes main texture features with moderate detail, and the secondary texture layer adds fine-grained patterns only where necessary. This localized application of quality levels maintains representation accuracy while reducing overall rendering complexity.
Solution Approach 2:
The patent implements partial action by including only the necessary level of texture detail for each land use type. Rather than uniformly applying maximum detail across all areas, the system incorporates texture features selectively based on the specific requirements of different land use classifications, achieving adequate representation accuracy without excessive processing.
Data Source
AI summary
A method for making texture symbols of a land use classification map is disclosed in the disclosure, including: capture of texture materials; extraction of main colors; color clustering; extraction of a texture skeleton; tile effect removal; and establishment of a texture library. The disclosure has the following advantages: definitions and classes of texture symbols are provided, and a procedure of making texture symbols is made clear; used natural texture symbols and symbolic texture symbols have clear semantic meanings, facilitating information transfer of the map; the quality and layering of the map are improved; the texture symbols can be directly used for production, to provide a fundamental support for survey and mapping of land use, and also to provide a solution for large-scale result mapping of natural resource survey. The expression of thematic maps of current land use classification is improved.