Essential LAS dataset vocabulary

This topic provides an overview of some of the terminology you will encounter when working with LAS datasets in ArcGIS.

LAS dataset terminology

Term	Description
Lidar	Lidar (light detection and ranging) is a remote-sensing technique that uses laser light to densely sample the surface of the earth with x,y,z measurements. Lidar datasets produce mass point datasets that can be visualized and analyzed using ArcGIS.
LAS format	LAS is an open/published standard file format for the interchange of lidar data. It is a binary file format that maintains specific information related to lidar data. It is a way for vendors and clients to interchange data and maintain all information specific to that data.
Triangulated irregular network (TIN)	A TIN is a vector data structure that partitions geographic space into contiguous, nonoverlapping triangles. The vertices of each triangle are sample data points with x, y, and z values. These sample points are connected by lines to form Delaunay triangles. TINs are used to store and display surface models and used as a background structure built on demand by terrains.
Surface constraints	Surface constraints are surface features stored in either geodatabase feature classes or shapefiles, which are used to enforce linear features in the LAS dataset surface.
Surface feature type	When adding a feature class to a LAS dataset as a surface constraint, you need to indicate its surface feature type. This defines the role the feature class plays in defining the LAS dataset surface. There are points, breaklines, and several polygon types.
Terrain dataset	A terrain dataset is a multiresolution, TIN-based surface built from measurements stored as features in a geodatabase.
Mosaic dataset	A mosaic dataset is a collection of raster datasets (and lidar files) stored as a catalog and viewed as a mosaicked image. The raster datasets can also be viewed individually. These collections can be extremely large both in total file size and number of raster datasets. The raster data is added according to its raster type, which identifies metadata, such as georeferencing, acquisition date, and sensor type, along with a raster format. The raster datasets in a mosaic dataset can remain in their native format on disk or, if required, be loaded into the geodatabase. The metadata can be managed within the raster record as well as stored as attributes in the attribute table. Storing metadata as attributes enables parameters such as sensor orientation data to be managed easily as well as enabling fast queries to enable selections.
Delaunay triangulation selections	This is a technique for creating a mesh of contiguous, nonoverlapping triangles from a dataset of points. Each triangle's circumscribing circle contains no points from the dataset in its interior. Delaunay triangulation is named for the Russian mathematician Boris Nikolaevich Delaunay.
Constrained Delaunay	A constrained Delaunay triangulation method follows traditional Delaunay rules everywhere except along breaklines. Using a traditional Delaunay triangulation method, breaklines are densified to ensure that the resulting triangulation remains Delaunay conforming. Therefore, one input breakline segment can result in multiple triangle edges. Using a constrained Delaunay triangulation, no densification occurs, and each breakline segment is added as a single edge.
ArcGIS 3D Analyst extension	This extension to ArcGIS provides tools for creating, visualizing, and analyzing GIS data in a three-dimensional (3D) context.
Point clouds	Large collections of spatially referenced point measurements captured by a remote sensing technique. Lidar data is known as point cloud data. These point clouds are large collections of 3D elevation points, which include x-, y-, and z-values along with additional attributes such as GPS time stamps. The specific surface features that the laser encounters can be classified after the initial lidar point cloud is post-processed.
Multipoint	A multipoint feature class stores many points in one database row.
Classification	Every lidar point that is post-processed can have a classification that defines the type of object that has reflected the laser pulse. Lidar points can be classified into a number of categories including bare earth or ground, top of canopy, and water. The different classes are defined using numeric integer codes in the LAS files. These class codes can be used as filters to display the lidar points referenced by the LAS dataset.
Filters	Lidar points of a LAS dataset can be queried and displayed based on certain filter criteria. Filter properties include area of interest, classification codes, classification flags, and lidar return values. The points that honor all the criteria pass through the filter for processing. For example, common filters are ground and aboveground, basically meaning ground laser returns and feature laser returns, respectively.

LAS dataset terminology