## 3 Considering the ALS Workflow and Its Impact on the Efficacy of Data Products
### Data Processing
#### 3.2.4 Point Cloud Classification  
The process of classification is also called segmentation or filtering, however these last two terms might not be fully accurate. Especially filtering, which generally implies removing an unwanted part of a signal. Point classification simply means assigning a specific class to each laser return or point. These classes might be based on several attributes, such as the ordinal number of the return (first, second, third, _etc._) produced by a shot, or by the number of returns produced per laser shot (only, first of many, intermediate, last), based on the strength of the return signal or intensity, or the return elevation above a certain arbitrary reference. However, for archaeology, and many other applications, the most common classification scheme is based on what produced the return, which could be the Earth's surface (ground), vegetation, building, power lines, _etc._ In general classification under this type of scheme is done in parts. First the returns are classified as coming from the ground or a non-ground surface, and then in a series of steps the non-ground returns can be classified as coming from vegetation, buildings, _etc._ For archaeology the first step has most relevance, and is usually the only step that is performed. Many ground/non-ground classifying algorithms have been proposed over the years, the reader is referred to [2,52,53] for extensive descriptions and experimental comparisons. Congruent to the central theme of this paper it is important to note that there is no single ground/no-ground classification algorithm that is the best algorithm under all conditions and applications. Perhaps one of the simplest and yet effective algorithms [2] that has been included in most ALS data classifying software was first described by [PERSON] in [54]. Due to its widespread implementation, we will use this algorithm to illustrate the crucial impact that the classification step can have on the quality/usefulness of the final data products. Illustrated in Figure 18, the [PERSON] algorithm is iterative and begins by creating a grid whose size is defined by a high probability that the lowest elevation return in every grid cell corresponds to a ground return. In urban and suburban areas this is generally defined by the size of the largest building in the project area, which ensures that the lowest elevation point does not fall on the  
Figure 17: **(a)** Point cloud from different flight strips, each strip has a different color; **(b)** Point cloud from a project file combining all the data for an area from different strips into a single tile, the PC is colored by elevation. building's roof. In the case of forested areas this size can be as small as twice the inverse of the expected ground return density, however slightly larger areas than the minimum are recommended to initialize the algorithm with true ground points. The algorithm then finds the lowest elevation return within each cell of the grid. From these lowest returns a triangulated faceted surface is built and it is assumed that its vertices are bare-ground returns. Then each point in the point cloud that is not a node on the triangulated facet, is evaluated based on the vertical distance and angle from the triangular facets it falls inside against filter parameters (which are tuned depending on the topographic and vegetation conditions), and then each returned to be classified as ground or non-ground. New ground points are added to the triangulated faceted model with each iteration of the algorithm. In a very generalized and simplified way the Axelsson algorithm generates a ground network by \"driving\" from one ground point to another, meaning that if there is a smooth path from a ground point to a candidate point, the candidate return will be tagged as a ground point. Of course some people will point out that such an algorithm will erode features that have vertical facets, such as stepped pyramids and staircases. However, algorithm parameters can be adjusted to account for these kinds of features (see Figure 19).