The proposed approach is experimentally validated using geographical condition census data for a city in southern China. Finally, the boundary of the structured geographic objects and the merging result of the area elements are inserted back into the aggregated result of the original land-cover data using the NOT operation. Second, a Miter-type buffer transformation is introduced to extract the boundary of each structured geographic object, and area elements inside the boundary are processed with corresponding merging operations. First, we identify the structured geographic objects based on the description parameters of the spatial structure. As such, this paper proposes an area merging method that can maintain the boundary characteristics of the structured geographic objects. However, current research often entails merging on a global perspective, which is not conducive to capturing the spatial characteristics of geographic objects with significant spatial structures, i.e., structured geographic objects. Merging is an important operation for the generalization of land-cover data. Our CPU-GPU system performs spatial join on two shapefiles, namely USA Water Bodies and USA Block Group Boundaries with 683K polygons in about 10 seconds using NVidia Titan V and Titan Xp GPU. PNP based task reduction and Striping algorithms filter out on average 95.84% of the workload of Point-in-Polygon tests. PolySketch filter reduces on average 99.77% of the workload of finding line segment intersections. After using R-tree, on average, our filter technique can still discard 69% of polygon pairs which do not have segment intersection points. We have implemented this filter and refine system on CPU and GPU by using OpenMP and OpenACC.

In addition to filtering polygons by their Minimum Bounding Rectangle (MBR), our hierarchical approach explores further filtering using tiles (smaller MBRs) to increase the effectiveness of filtering and decrease the computational workload in the refinement phase. We have extended the classical filter and refine algorithms using PolySketch Filter to improve the performance of geospatial computations. These intersections are the compute-intensive spatial data analytic kernels in spatial join and map overlay computations.

The inputs are two layers of large polygonal datasets and the computations are spatial intersection on a pair of cross-layer polygons. In this paper, we introduce our hierarchical filter and refinement technique that we have developed for parallel geometric intersection operations involving large polygons and polylines.

Burraco Software Contro Computer