Land Use Patch Generalization Based on Semantic Priority

Abstract

Land use patch generalization is the key technology to achieve multiscale representation. We research patches and achieve the following. (1) We establish a neighborhood analysis model by taking semantic similarity between features as the prerequisite and accounting for spatial topological relationships, retrieve the most neighboring patches of a feature using the model for data combination, and thus guarantee the area of various land types in patch combination. (2) We establish patch features using nodes at the intersection of separate feature buffers to fill the bridge area to achieve feature aggregation and effectively control nonbridge area deformation during feature aggregation. (3) We simplify the narrow zones by dividing them from the adjacent feature buffer area and then amalgamating them into the surrounding features. This effectively deletes narrow features and meets the area requirements, better generalizes land use features, and guarantees simple and attractive maps with appropriate loads. (4) We simplify the feature sidelines using the Douglas-Peucker algorithm to effectively eliminate nodes having little impact on overall shapes and characteristics. Here, we discuss the model and algorithm process in detail and provide experimental results of the actual data.

SPLITAREA: an algorithm for weighted splitting of faces in the context of a planar partition

“…A lot of research has been devoted to determine when objects are compatible so they can be aggregated (e.g. Van Smaalen 2003, Vermeij et al 2003, Yaolin et al 2003, Schwering 2008, Haunert and Wolff 2010, Yang et al 2013. In the original GAP-tree (Van Oosterom 1993), the compatibility between a specific face and one of its neighbours, neighbour i (at instance level), is defined with three terms to determine with which neighbour a face should be merged:…”