## 4 ALS Data Visualization, Analysis and Quality/Accuracy Assessment
### Data Visualization
The second row presents greyscale and color image maps of the slope computation performed in a slightly different way, which consists of computing the average slope considering five adjacent cells in eight different directions, and storing the maximum value obtained from the different directions. The third row presents data computed using the sky view factor (SVF) considering eight directions and five cells. In the fourth row are results from an algorithm produced by the authors that attempts to estimate the height anomaly of a raster cell with respect to adjacent cells separated by a specially defined \"window\" and sliding that window along the row, column, and diagonal directions. As can be seen in Figure 23 different features are highlighted by the different methods. The detailed features in the result of a given method can be more or less evident if a greyscale or color scale is used. Different people will have an easier time identifying features in either color or greyscale. This might be due to physiological differences in the individual's vision system or a matter of being more familiar with one form of scaling than the other. Greyscale produces what seems to be a natural gradient that seems to be more intuitive. Color scales are artificial, but carefully designed color scales can be used to highlight peculiar or anomalous terrain features. Methods that substitute objective computer analysis for the subjective nature of human vision are probably the best way to overcome the visualization limitations of current methods. For those individuals that prefer human analysis, a few points need to be kept in mind. There is no single visualization technique or data analysis mechanism that will enhance or highlight every single feature of interest in an elevation raster. Some will work better than others for specific features [62, 67]. Also, techniques such as SVF, Maximum Slope, and height anomaly, are scale specific, meaning that they all take into account data at a certain spatial extent and as such will be sensitive to specific spatial scales. These techniques need to be optimized for specific features of specific sizes. There is no one size fits all approach. As a final note on visualization, we want to point out a different kind of visualization technique that has great potential but whose use in archaeology needs to be assessed and developed. We are referring  
Figure 24: Photo of a plastic 3D print of the main Pyramid in Mayapan. to the additive fabrication technologies commonly known as 3D printing. High quality 3D printing may enable the visualization of natural and manmade topographic features in full three dimensions that are not easily represented on a 2D computer display. The 3D prints have high educational and display value and can also be used as a tool for the visualization of archeological features by visually impaired individuals. Figure 24 shows a 3D print of the main pyramid of Mayapan in Yucatan Mexico. The print was created on a Dimension SST printer that works with plastic, the white plastic was then hand colored by an artist to obtain a more realistic look. For this print the vegetation growing out from the cenote (brown depression adjacent to the closer edge of the 3D) was removed to reveal part of the cenote structure captured by the ALS.