Wil Blouin's LiDAR Portfolio

Background and Goals: This lab focused on the process of creating and enforcing hard breaklines for the production of LIDAR derivatives. The lab covered the creation, identification, and fixing of potential topological errors in breaklines. It then worked through the process of conflation of those breaklines and configuring the enforcement of the breaklines for production of the derivative and the production of those derivatives: contours and digital terrain models (DTMs). This lab used both LAS and breakline data from both Eau Claire and Lake County, IL datasets.  Methods:      Part 1: Breakline QA/QC and elevation conflation           Section 1: This section utilized optical imagery to perform QA/QC on soft breaklines. Imagery was brought into ArcMap and breaklines were properly configured so that islands were digitized as holes in polygon shapes. This was performed with the Illinois dataset. Figure 1: Islands incor...

Goals and Background: This lab consisted of practicing performing synoptic QC measures including producing a point cloud density image, and producing statistics for local areas in the point cloud data using a stamp tool. Also practiced in this lab were relative accuracy assessment procedures, and absolute accuracy assessment procedures. Relative accuracy assessment consisted of creating a delta Z (dZ) ortho image raster and determining swath-to-swath accuracy in non-vegetated terrain as a root-mean-square difference (RMSD z ). Absolute accuracy consisted of obtaining non-vegetated vertical accuracy and horizontal accuracy. Finally, manual cleanup was performed. The dataset worked on in the past three labs was the dataset assessed in this lab.  Quality assurance (QA) and quality control (QC) are two different processes that ensure that overall accuracy goals of a LiDAR data are met or exceeded. Quality assurance consists of care taken with events taking place up to the end of a...

Goals and Background: In this lab vegetation is classified in the LAS dataset from labs 1 and 2. The height filter and the manual cleanup are used to complete this task. Methods: Part 1: Automatic height filtering of vegetation object points Automatic height filtering, a very basic filter for categorization of points into height classes is used for the first, most automated portion of this lab. A filter using the Z values of the points above the classified ground points and thresholds for different levels of classification (low, medium, and high) is used to classify these points. This is why it is important to have correct ground classification properly performed before running this filter. Table 1 shows the different classes of vegetation. Table 1 Figure 1 shows these classifications applied in the filter window. Figure 1 Part 2: Manual cleanup of the vegetation classification This part of the lab involved using the same basic filter tool to clean up omission a...