Wil Blouin's LiDAR Portfolio

Goals and Background:  This lab practiced the classification of buildings in the same LAS point cloud dataset where ground was previously classified in Lab 1. The planar filter was used to classify the building points in LP360. After this filter was run, manual cleanup was performed on the dataset. Methodology: Part 1: Execution of the planar point filter The planar point filter was used to classify buildings using the macro task type in the point cloud task window. A new macro task was made and then a planar point filter subtask. The unclassified class was used for source points, the ground class for ground points, and the building class for destination class. The maximum height parameter was calculated using the elevation of the tallest building with the lowest elevation found that was still classified as ground subtracted. This made sure that no taller vegetation was classified as buildings. The rest of the parameters were obtained form recommendations in the assi...

Goals and Background: The main objective for this lab is to classify LAS preprocessed points into water and ground points which could then be used in many different applications. To do this the ground filtering algorithm is used to classify ground points and remove low outliers, manual cleanup is employed to complete classification, and water body feature breaklines are used to identify water points. Methodology: Part 1: All LAS files from a flight over Lake County, Illinois were brought into LP360 64-bit for Windows with read and write abilities along with a raster NAIP image of the study area for contextual information. The data was then displayed by classification to see the initial state of the data: all points had not been classified ( Figure 1 ). Figure 1: All points unclassified The live view window was then opened, and after filtering by all points the autoselect all classes being used on active LAS layer button was selected to show the classes the points displaye...