A method of automatic downsizing of land use data

Abstract

The invention relates to the technical field of map downsizing, in particular to an automatic downsizing method for land use data, which firstly extracts housing construction areas and water systems from original data to perform independent downscaling, and then performs scale transformation through resampling and reclassification based on a grid pattern, and then Then, the fragmentation processing is performed, and finally the data downscaling result is obtained based on the vector data structure through the spatial adjacency relationship and the semantic adjacency relationship, and the downsizing result can be obtained. The semantic information of the patches is maintained, and the regional geographic features and element relationships of the patches are kept correct, so as to meet the requirements of high-quality land use map compilation.

Description

technical field [0001] The invention relates to the technical field of map downsizing, in particular to an automatic downsizing method for land use data. Background technique [0002] A land use map is a thematic map that expresses the utilization status, regional differences and classification of land resources. When the map is compiled, it is necessary to downsize the large-scale land use data into different small-scale data according to different paper formats and different administrative regions. Because the land use data has the characteristics of no overlap, no gap, full coverage and large quantity, high-quality data can be achieved. The automatic downsizing of the technology poses great challenges to the process design, algorithm design, strategy design and equipment environment. At present, the most commonly used methods for automatic downsizing of land use data are based on spatial proximity and semantic proximity, calculating proximity models, establishing compreh...

AI Technical Summary

Problems solved by technology

At present, the most commonly used methods for automatic downsizing of land use data are based on spatial proximity and semantic proximity, calculating proximity models, establishing comprehensive knowledge rules, and developing tool sets such as patch merging and graph bending simplification to improve the automation of downsizing, similar to The solution is very dependent on the platform architecture and hardware environment in the face of massive map spots (more than 100,000). In actual operation, it usually takes several days to get the processing results, and the processing process is prone to memory overflow and system freezing. When the scope is large, it is necessary to perform physical partition and block first, and then carry out complex data seam repair. The processing process and parameter setting are very complicated. This process still requires a lot of manual participation, which seriously affects the operation efficiency.

Other automatic downsizing methods, such as automatic fusion and aggregation of terrain types and patches based on raster calculations to realize automatic comprehensive downsizing, this method less considers the geometric shape and distribution characteristics of elements, and can not take into account semantic information such as map spots level and affiliation , the geographical features are poorly maintained, and the synthesis of special maps such as narrow and long maps is unreasonable, resulting in hard edges and poor results after synthesis, which is difficult to directly apply to the production of land use maps

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