Lab 8: Corridor analysis and feature extraction
Goals and Background:
This lab practiced the skill of corridor analysis using LiDAR point cloud data from a terrestrial LiDAR scanner (TLS). Other skills that were practiced included projection of point cloud data and extraction of building footprints. The extraction of building footprints included finding building footprint features with Z characteristics that would make the buildings eligible for a letter of map amendment (LOMA). Details on LOMAs can be found here.
Methods:
Part 1: Projecting an unprojected point cloud
This part made simple use of the LP360 tools included in the license for LP360 for Arcmap. All of the LP360 tools available were added to a new toolbox, and the Define LAS File Projection and then Reproject LAS Files tools were used. The projection specified in the readme file for the Algoma,WI LAS dataset was defined, and then the transverse mercator projection was used in the reprojection. In using both of these tools horizontal and vertical coordinate systems were defined.
Part 2: Transport and transmission corridor asset management
In this part of the lab, the Algoma, WI TLS dataset was displayed in LP360. Using the 3D window, the profile view, and the other display tools available, the dataset was observed. Specific features and characteristics such as light poles, width of bridges, and other items were able to be observed.
Part 3: Building Feature Extraction
This part made use of the Point Group Tracing and Squaring point cloud task algorithm to trace the outlines of the classified buildings from Lab 2 (see previous blog). This process generated a building footprint and a squared off footprint feature. These features were subsequently conflated using the summarize Z method to obtain minimum and maximum Z values for each feature and the pure drape method to add Z values to the vertices of the building features. From the elevation data generated for the buildings, the buildings that could be taken off of flood plain insurance maps were able to be found using selection in ArcMap.
Results:
Parts 1 and 2:
For practice in navigating and interpreting terrestrial LiDAR data, light poles, signage, and bridge characteristics were counted and measured. Also observed was vegetation encroachment on powerlines. The below figure shows the 3D view in which this was practiced.
Part 3:
Sources:
This lab practiced the skill of corridor analysis using LiDAR point cloud data from a terrestrial LiDAR scanner (TLS). Other skills that were practiced included projection of point cloud data and extraction of building footprints. The extraction of building footprints included finding building footprint features with Z characteristics that would make the buildings eligible for a letter of map amendment (LOMA). Details on LOMAs can be found here.
Methods:
Part 1: Projecting an unprojected point cloud
This part made simple use of the LP360 tools included in the license for LP360 for Arcmap. All of the LP360 tools available were added to a new toolbox, and the Define LAS File Projection and then Reproject LAS Files tools were used. The projection specified in the readme file for the Algoma,WI LAS dataset was defined, and then the transverse mercator projection was used in the reprojection. In using both of these tools horizontal and vertical coordinate systems were defined.
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| Figure 1: Algoma, WI terrestrial LiDAR dataset studied in Part 1 and Part 2 |
Part 2: Transport and transmission corridor asset management
In this part of the lab, the Algoma, WI TLS dataset was displayed in LP360. Using the 3D window, the profile view, and the other display tools available, the dataset was observed. Specific features and characteristics such as light poles, width of bridges, and other items were able to be observed.
Part 3: Building Feature Extraction
This part made use of the Point Group Tracing and Squaring point cloud task algorithm to trace the outlines of the classified buildings from Lab 2 (see previous blog). This process generated a building footprint and a squared off footprint feature. These features were subsequently conflated using the summarize Z method to obtain minimum and maximum Z values for each feature and the pure drape method to add Z values to the vertices of the building features. From the elevation data generated for the buildings, the buildings that could be taken off of flood plain insurance maps were able to be found using selection in ArcMap.
Results:
Parts 1 and 2:
For practice in navigating and interpreting terrestrial LiDAR data, light poles, signage, and bridge characteristics were counted and measured. Also observed was vegetation encroachment on powerlines. The below figure shows the 3D view in which this was practiced.
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| Figure 2: Algoma, WI bridge as seen in the 3D viewer of LP360 |
Hypothetical circumstances were defined in the lab instructions. These detailed a situation where the requirement for a LOMA changed from 810 feet above sea level (ASL) to 800 feet ASL. The map below was made to show the newly included homes for which a LOMA could be filed. This class of buildings represented 5.333% of buildings in the study area.
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| Figure 3: Buildings between 800 and 810 feet above sea level that qualify for a LOMA under hypothetical circumstances |
- Lab instruction provided by Dr. Cyril Wilson.
- Terrestrial LiDAR data from Ayers Associates.
- Refer to blogs created from labs 1 through 4 for sources of Lake County, IL data.
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