Lab 2 - Building Classification

GOAL AND BACKGROUND

The goal of this lab was to learn how to classify buildings in LiDAR data. This would be done with the data that ground was already classified, which was done in the first lab. Some basic QA/QC would be necessary to minimize error.

METHODS

LP360 was used for this lab. A planar point filter was used to classify buildings. Basic parameters were set, such as a height filter. The purpose of the height filter was to cut out planar surfaces low enough that would not be buildings, such as cars, and high enough that high noise would be ignored. Other parameters, such as minimum edge plane, grow window area, and N fit were also set and adjusted to best filter the data.

The results are shown and discussed below.

RESULTS

The entire study area after building classification is shown below. Buildings are classified by red, ground by orange, water by blue, and gray areas are unclassified, which at this point is vegetation.

Below are a few examples of closer views of buildings within the study area. First is a building that was shown as previously unclassified in the lab 1 post, now classified accurately.

The next image shows residential housing that was classified. Note that residential housing had trouble being classified with the algorithm, so a lot of manual cleanup was necessary.

SOURCES

Data obtained from Cyril Wilson for use in 358 LiDAR course.