What is the most efficient data model for managing elevation data with three billion points?

The most efficient data model for managing elevation data with three billion points is a terrain dataset. Terrain datasets are specifically designed to handle large amounts of elevation data, such as LiDAR or other point cloud data, in a more efficient manner by allowing for advanced analysis and visualization of the surface. They enable the storage and management of elevation data while providing features like TIN (Triangulated Irregular Network) representation, which facilitates various surface analysis tasks.

Additionally, terrain datasets optimize the storage and querying of data points, improving performance when dealing with extensive datasets. They can efficiently represent large areas and can be integrated with other datasets to create detailed 3D models, making them particularly suitable for managing elevation data at the scale specified in the question.

In contrast, while triangulated datasets can represent elevation data, they may not handle extremely large datasets as efficiently due to their structure. Raster datasets can manage elevation data through grid systems but might require substantial storage and can be less efficient in processing at this scale. Mosaic datasets are generally used for managing collections of images or raster data, which are not specifically optimized for elevation point data management like a terrain dataset is.