A Graph-based Approach for Higher Order Gis Topological Analysis

Retrieving structured information from an initial random collection of objects may be carried out by understanding the spatial arrangement between them, assuming no prior knowledge about those objects. As far as topology is concerned, contemporary desktop GIS packages do not generally support further analysis beyond adjacency. Thus, one of the original motivations of this work was to develop new ideas for scene analysis by building up a graph-based technique for better interpretation and understanding of spatial relationships between GIS vector-based objects beyond its first level of adjacency; the final aim is the performance of some kind of local feature organization into a more meaningful global scene by using graph theory. As the example scenario, a LiDAR data set is being used to test the technique that we plan to develop and implement. After the generation of the respective TIN, two different binary classifications were applied to the TIN facets (based on two different slope thresholds) and TIN facets have been aggregated into homogeneous polygons according to their slope characteristics. A graph-based clustering procedure inside these polygonal regions, by establishing a neighbourhood graph, followed by the delineation of cluster shapes and the derivation of cluster characteristics in order to obtain higher level geographic entities information (regarding sets of buildings, vegetation areas, and say, land-use parcels) is object of further work. The results we are expecting to obtain might be useful to support land-use mapping, image understanding or, generally speaking, to support clustering analysis and generalization processes