We propose a family of spatial data structures for the representation and processing of Triangulated Irregular Networks (TINs). We call such data structures Terrain trees. A Terrain tree combines a minimal encoding of the connectivity of the TIN with a hierarchical spatial index. Connectivity relations are extracted locally at run-time, within each leaf block of the hierarchy, based on specific application needs. Spatial queries are performed by exploring the hierarchical data structure. We present a new framework for terrain analysis based on Terrain trees. The framework, implemented in the Terrain trees library (TTL), contains algorithms for morphological features extraction, such as roughness and curvature, and for topology-based analysis of terrains. Moreover, it includes a technique for multivariate visualization, which enables the analysis of multiple scalar fields defined on the same terrain. To prove the effectiveness and scalability of such framework, we have compared the different Terrain trees against each other and also against the most compact state-of-the-art data structure for TINs. Comparisons are performed on storage and generation costs and on the efficiency in performing terrain analysis operations.

我们提出了一系列空间数据结构，用于不规则三角网络 (TIN) 的表示和处理。我们称这种数据结构为地形树. 地形树将 TIN 连通性的最小编码与分层空间索引相结合。根据特定的应用程序需求，在运行时，在层次结构的每个叶块中本地提取连接关系。通过探索分层数据结构来执行空间查询。我们提出了一个基于地形树的地形分析新框架。该框架在地形树库 (TTL) 中实现，包含用于提取形态特征的算法，例如粗糙度和曲率，以及基于拓扑的地形分析算法。此外，它还包括一种多变量可视化技术，可以分析在同一地形上定义的多个标量场。为了证明这种框架的有效性和可扩展性，我们将不同的地形树相互比较，并与最紧凑的最先进的 TIN 数据结构进行比较。对存储和发电成本以及执行地形分析操作的效率进行了比较。