Mustafa YILMAZ
University of Seoul, Business Administration, Undergraduate
Building roof plane extraction from Light Detection And Ranging (LiDAR) data has become very popular study. One of most popular algorithms for planar feature extraction is Random Sample and Concencus (RANSAC). The RANSAC algorithm defines... more
Building roof plane extraction from Light Detection And Ranging (LiDAR) data has
become very popular study. One of most popular algorithms for planar feature extraction is
Random Sample and Concencus (RANSAC). The RANSAC algorithm defines the planes in
a continuous infinite planimetric space, and the points on the continuing plane planimetry,
which are not within the bounds of the plane, are extracted as if they are in plane boundary.
The aim of this study is to develop an algorithm for defining and eliminating the outliers
from building roof planes, which are extracted using RANSAC algorithm to
enhance/improve RANSAC plane extraction results. Hence, an algorithm was develop
(called as Improved-RANSAC, I-RANSAC) to enhance RANSAC algorithm for planar
feature extraction. To extract planar feature from Lidar data, ground and non-ground points
need to be classified. Using only non-ground points from the whole LiDAR data, the
proposed plane extraction algorithm (I-RANSAC) was tested with 8 single building LiDAR
data and 3 LiDAR data sets that contain more than one buildings. Precision, Recall and Fmeasures
are calculated and observed as around 0.95.
become very popular study. One of most popular algorithms for planar feature extraction is
Random Sample and Concencus (RANSAC). The RANSAC algorithm defines the planes in
a continuous infinite planimetric space, and the points on the continuing plane planimetry,
which are not within the bounds of the plane, are extracted as if they are in plane boundary.
The aim of this study is to develop an algorithm for defining and eliminating the outliers
from building roof planes, which are extracted using RANSAC algorithm to
enhance/improve RANSAC plane extraction results. Hence, an algorithm was develop
(called as Improved-RANSAC, I-RANSAC) to enhance RANSAC algorithm for planar
feature extraction. To extract planar feature from Lidar data, ground and non-ground points
need to be classified. Using only non-ground points from the whole LiDAR data, the
proposed plane extraction algorithm (I-RANSAC) was tested with 8 single building LiDAR
data and 3 LiDAR data sets that contain more than one buildings. Precision, Recall and Fmeasures
are calculated and observed as around 0.95.
