Height-based vector vegetation segmentation is one of the critical aspects of spatial analysis. This segmented data is used in radio propagation modeling, environmental monitoring, and vegetation mapping. Many studies on vector vegetation segmentation focus on delineating individual tree crowns, allowing detailed data sets to be obtained. However, the high level of detail results in a substantial data volume, making it impractical to use these datasets over large areas, such as an entire country. Segmentation of large vector data sets remains a significant challenge in geospatial data creation. In our study, we developed three different segmentation pipelines: hexagon segmentation, convolution segmentation, and random points. A test data fragment was processed to compare the proposed methods and accuracy and volume metrics were calculated.