Premium
Looking for a needle in a haystack: Probability density based classification and reconstruction of dormers from 3D point clouds
Author(s) -
Dehbi Youness,
Koppers Sonja,
Plümer Lutz
Publication year - 2021
Publication title -
transactions in gis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.721
H-Index - 63
eISSN - 1467-9671
pISSN - 1361-1682
DOI - 10.1111/tgis.12658
Subject(s) - point cloud , roof , haystack , computer science , point (geometry) , lidar , distortion (music) , exploit , artificial intelligence , remote sensing , geology , geography , mathematics , geometry , amplifier , computer network , archaeology , bandwidth (computing) , computer security
Accurate reconstruction of roofs with dormers is challenging. Without careful separation of the dormer points from the points on the roof surface, the estimation of the roof areas is distorted. The characteristic distortion of the density distribution in comparison to the expected normal distribution is the starting point of our method. We propose a hierarchical method which improves roof reconstruction from LiDAR point clouds in a model‐based manner, separating dormer points from roof points using classification methods. The key idea is to exploit probability density functions to reveal roof properties and to skilfully design the features for a supervised learning method using support vector machines. The approach is tested based on real data as well as simulated point clouds.