Corridor Analysis & Feature Extraction

Goals and Background

The goal of the lab is to introduce me to necessary skills required to analyze corridor lidar point cloud data. I will be introduced to the steps required to project a LAS dataset and then gain practical experience utilizing a terrestrial lidar scan. The second half of the lab I will b extracting building footprints for a different data set and extracting LOMA information from the results.

Methods

Projecting an unprojected point cloud

The following methods were performed in ArcMap 10.4.1. A backup copy of the data was made before performing any of the following steps.

I was provided a terrestrial lidar scan which was not projected. The first step was to add LP360 Tools to My Toolbox within ArcMap (Fig. 1).

(Fig. 1) LP360 tools added to My Toolboxes in ArcMap.

The data set did not have any coordinate information assigned to the file. I consulted the Metadata to determine what coordinate system the data was collected in. Then with the Define LAS File Projection tool I input the LAS file and set the coordinate system per the Metadata (Fig. 2).

(Fig. 2) Define LAS File Projection tool used to define the projection of the LAS file.

After defining the projection then I had to use the Reproject LAS Files tool to permanently project the LAS data (Fig. 2). The input LAS file was the Defined file from the previous step. The coordinate system was set again per the metadata in both the incoming and outgoing parameter.

Transport and transmission corridor asset management

The following methods were performed in LP360 for Windows. I used the projected file of the terrestrial lidar data from the previous step.

I opened the LAS file in LP360 (Fig. 3). The data of a terrestrial dataset does not look any different than conventional lidar data in the traditional view. To see the variation of terrestrial lidar data you have to examine the data through the 3D viewer (Fig. 4)

(Fig. 3) Terrestrial dataset opened in LP360 displayed by elevation.

(Fig. 4) Terrestrial dataset displayed in the 3D viewer in LP360.

(Fig. 5) Terrestrial dataset displayed in the 3D viewer in LP360. Zoomed in view to a bridge within the study area. Notice the street lamps and road sign.

I used the measurement tool in LP360 to measure the bridge and sidewalk widths in the study area. Additionally, I panned around the 3D window to examine areas where utility lines had the potential to be damaged by trees in the event of a storm (Fig. 6).

(Fig. 6) Examination of trees encroaching on utility lines in the study area.

Building Feature extraction

I used LP360 for Windows again in this section of the lab. I used my classified lidar dataset from Lab 3.

To extract the building footprints I created a new point cloud task. The parameters were set as shown in Fig. 7.

(Fig. 7). Point cloud task window with the parameters set to extract the building outlines form the study area.

The result produces two shapefiles which include a building footprint and building footprint square (Fig. 8 & 9). The footprint is based on the exact classification. The footprint square makes generalizations of the footprint based on the classification and conventional building shape by creating 90 degree corners.

(Fig. 8) Footprint and footprint square displayed in LP360.

(Fig. 9) Footprint and footprint squared displayed in LP360. The image is zoomed in to display the difference in the footprint (yellow) and the footprint square (blue).

Extracting building height and LOMA information

The following methods were preformed in LP360. The purpose of the section is to deteremine the buildings were can apply to be excluded from the LOMA floodplain map since the height requirement was changed from 810 feet ASL to 800 feet ASL.

The first step to extracting building heights was to create a new point cloud task. I used a Conflation task with the parameters set as per Fig. 10.

(Fig. 10) Parameters used in the conflation task to extract the minimum and maximum height values using the building square footprint.

I then created another new conflation task in the point cloud tasks. The parameters were set as displayed in Fig. 11.

(Fig. 11) Parameters used in the second conflation task.

Finally I created one last conflation task in the point cloud task window. The parameters were set as displayed in Fig. 12.

(Fig. 12) Parameters used in the last conflation task to calculate the minimum z value for the buildings in the study area.

Results

(Fig. 13) Map displaying buildings which are below the 810 foot ASL LOMA requirement.

(Fig. 14) Map displaying buildings which are below the revised 800 foot ASL requirement.

Sources

Terrestrial LAS for Algoma, WI, project boundary KMZ, and metadata are from Ayres Associates.

LAS, tile index, and metadata for Lake County are from Illinois Geospatial Data ClearingHouse. NAIP imagery is from United States Department of Agriculture Geospatial Data Gateway. Breaklines is from Chicago Metropolitan Agency for Planning.