Dynamic hybrid terrain representation based on convexity limits identification

Abstract

Hybrid digital terrain models combine terrain data with different topologies and resolutions. Cartographic digital terrain models are typically composed of regular grid data that can be locally refined by adding a Triangulated Irregular Network (TIN) that represents morphologically complex terrain parts. Direct rendering of both datasets to visualize the digital terrain model generates discontinuities, as the meshes are disconnected. The utilization of complete/partial precomputed tessellation solutions solves the problem of quality, but limits the applicability of the representation to models with a fixed relative position between datasets. In this paper, we present a new scheme for hybrid terrain representation that permits the dynamic generation of the adaptive tessellation required to join the grid and TIN models. Our proposal permits the dynamic modification of the relative position between datasets. This increases the representation capabilities for those applications where this property is interesting as, for example, urban and landscape planning applications. The algorithm we propose is based on the identification of convex areas on the TIN and the efficient generation of triangles to join the models based on this convex structure. As a result, high quality models without discontinuities are obtained, increasing the flexibility of previous solutions based on fixed precomputations.