In this scenario, you have LIDAR points and photogrammetric breaklines stored in ASCII text files. You’ll use them to construct a terrain. To accomplish this, you need to import them into feature classes that reside in a feature dataset. The terrain will be made at the same location, next to its source data. You’ve been provided a file geodatabase with a feature dataset. It contains two polygon feature classes. One is for lakes, the other to delineate the study area. What you need to do first is import the ASCII points and lines.

Next you’ll convert points from a simple text file in XYZ format into a multipoint feature class. These points only define surface geometry. All that’s recorded for them are x, y, and z. There’s one point per line, and the coordinates are separated by spaces. Since there is no attribution associated with these points, dedicating a database row for each is wasteful and inefficient. Instead, you’ll combine them into multipoints. A multipoint can store many points per shape or row, saving storage space and improving read-write performance.

The point spacing represents the average distance between measurement points. Sometimes this is referred to as nominal point spacing. This is given in the xy units of the data. The average point spacing option is only available when the Output Feature Class Type parameter is set to MULTIPOINT. It facilitates the clustering of points so that each output multipoint is made from points that are relatively close to one another.

If you need to process a large collection of LIDAR points, consider using the LAS format instead of XYZ. LAS is an industry-standard format for LIDAR data. It’s more efficient because it’s binary. It also has more information stored in the file about the data. There’s a separate tool to import LAS format files, LAS to Multipoint, that’s located in the same toolset as the ASCII 3D to Feature Class tool.