Performance of Greedy Triangulation Algorithm on Reconstruction of Coastal Dune Surface

Reconstruction and visualization of geographical structures have been a challenging problem because of the size and format of the data dealing with it. Advancement in imaging and sensor technology can now generate high-resolution point data about geographical features ranging from very small to extremely large structures. Generating a triangulated surface is often more efficient in mapping the surface to its topographical attributes and performing analytical studies. This paper evaluates the novelties of applying a greedy projection-based algorithm for reconstructing regions of coastal dune acquired as geospatial point cloud data from LiDAR sensors. This paper highlights the importance Kd-tree search, local gridding, surface angle and searches radius limits for reconstructing coastal dune region. The performance of greedy triangulations validated on the data for the Coastal Dune fields of Garopaba and Vila Nova located at Santa Catarina State in Brazil. The experimental results suggest that while greedy projection based triangulation is suited for reconstructing coastal dune for performance reasons, a small patch of surface holes exists in the resulting surface. The reconstruction performance is between 93.4% - 96.8%.