## 2 Understanding the Jargon: Equipment and Data Products
### Understanding ALS Data Products  
To continue this descriptive process we will focus on the final data products. These data products represent the \"digital artifacts\" from which an archeologist starts his/her geospatial investigation. Similar to physical artifacts, the clues provided by ALS data depend on the quality and density of the observations. However, the difference is that the researcher has some control on the quality and density of the data collected and can thus optimize them for the research questions they intend to answer. At this point, we want to point out that the word artifact has very different connotations in the archaeological and engineering worlds. In archeology an artifact is an object that represents a piece of the puzzle and hence a very positive thing. However, for a LiDAR engineer, the word artifact is used to denote an anomaly or error in the LIDAR data and hence something negative that needs to be minimized or eliminated. The first ALS data product is the point cloud (PC). The PC is by nature an irregularly spaced dataset, with the location of each laser return resulting from the scanning geometry, the dynamics of the aircraft and the nature and structure of the intercepted target (vegetation, ground or manmade structure). The PC is just a swarm of points (one per laser return), and in its crudest form is just a collection of X, Y, Z coordinates for each return. More elaborate point clouds may include some ancillary information for each return, such as shot GPS time, return intensity, scan angle, ordinal return number of the shot (first, second,... last), class it belongs to, return roughness (in the case of full waveform digitizing systems), _etc._ The raw point cloud is further processed by classifying the returns depending on the type of object that produced each reflection (ground, vegetation, buildings, _etc._) The most common and perhaps most important classification is the one that separates the returns as coming from the ground or non-ground objects. The fully classified PC represents the ALS dataset with the highest density of information and is fully 3-dimensional. As illustrated in Figure 5, a PC can be visualized as simple points or as a triangulated irregular network (TIN), which is generated by joining neighboring points with an array of triangular facets. A TIN is created in such a way that each facet has the smallest possible area, which is achieved by having the three closest points defining the vertices of the facet. From the classified irregularly spaced point cloud it is possible to create regularly spaced rasters (grids) for which a single value of elevation is assigned to each raster cell or grid node. The elevation of each cell is computed by interpolating the irregularly spaced elevation values from the PC. Because these rasters only allow a single elevation value per grid cell and do not represent the full complexity of the PC these datasets are considered to be 2.5-Dimensional. They also have the disadvantage that they only represent a small fraction of the information contained in the point cloud. Because they condense the information of the PC, and because of their regular spacing, rasters are easier to manipulate, visualize and analyze using established image processing techniques. Most researchers might be familiar with these rasters which can be displayed in a variety of ways, including image maps, shaded relief maps (hill shades) and contour maps. Depending on what surface these elevation rasters represent they receive specific names such as digital elevation models (DEM), digital surface model (DSM) and digital terrain model (DTM). The usage of these terms might be confusing and some people use them interchangeably. This confusion in part arises because of the opposite general usage of the terms DEM and DTM in the United States and Europe. Here we will attempt to define and explain them in accordance with the usage accepted by the American Society for Photogrammetry and Remote Sensing (ASPRS) and described in [35].