Journal ArticleDOI
Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds
G. Sithole,George Vosselman +1 more
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TLDR
In general, filters that estimate local surfaces are found to perform best and should be directed towards the usage of additional data sources, segment-based classification, and self-diagnosis of filter algorithms.Abstract:
Over the past years, several filters have been developed to extract bare-Earth points from point clouds. ISPRS Working Group III/3 conducted a test to determine the performance of these filters and the influence of point density thereon, and to identify directions for future research. Twelve selected datasets have been processed by eight participants. In this paper, the test results are presented. The paper describes the characteristics of the provided datasets and the used filter approaches. The filter performance is analysed both qualitatively and quantitatively. All filters perform well in smooth rural landscapes, but all produce errors in complex urban areas and rough terrain with vegetation. In general, filters that estimate local surfaces are found to perform best. The influence of point density could not well be determined in this experiment. Future research should be directed towards the usage of additional data sources, segment-based classification, and self-diagnosis of filter algorithms.read more
Citations
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Dissertation
Web-based GIS modelling of building-integrated solar photovoltaic system for the City of Cape Town
Proceedings ArticleDOI
The generation of digital terrain models from LiDAR data using seeding and filtering and its application to flood modelling
Jinhai Cai,Stan Miklavcic +1 more
TL;DR: This study presents a novel unsupervised algorithm for digital terrain model generation, which combines the advantages of the seed- based filtering and the slope-based filtering to classify objects and ground points.
Proceedings ArticleDOI
An improved 1D filtering method for LIDAR point cloud
TL;DR: In this paper, an improved 1-D filtering method is proposed to separate non-ground points from raw LIDAR point cloud for the purpose of improving processing efficiency and precision.
Proceedings ArticleDOI
A Review of Data Structure and Filtering in Handling 3D Big Point Cloud Data for Building Preservation
S. N. Mohd Isa,S. A. Abdul Shukor,Nasrudin Abd Rahim,Ismail Maarof,Zainor Ridzuan Yahya,Ammar Zakaria,Abu Abdullah,R. Wong +7 more
TL;DR: This paper will review developed methods in handling 3D point cloud data, concentrating on two specific processes, which are data structure and data filtering, and results will be studied to explain the effectiveness of the methods used in handling big point data.
Impact of optimization of ALS point cloud on classification
W. Błaszczak-Bąk,A. Sobieraj +1 more
TL;DR: In this study the main goal was to test the impact of optimization on the results of a classification of airborne laser scanning point cloud processing, and an optimization algorithm was performed in the original point cloud.
References
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Journal ArticleDOI
Determination of terrain models in wooded areas with airborne laser scanner data
K. Kraus,Norbert Pfeifer +1 more
TL;DR: In this article, the characteristics of laser scanning are compared to photogrammetry with reference to a big pilot project and the results are in accordance with the expectations, however, the geomorphologic quality of the contours, computed from a terrain model derived from laser scanning, needs to be improved.
Journal ArticleDOI
Processing of laser scanner data-algorithms and applications
TL;DR: This paper presents some methods and algorithms concerning filtering for determining the ground surface, DEM, classification of buildings for 3D City Models and the detection of electrical power lines.
Slope based filtering of laser altimetry data
TL;DR: In this article, a new method is proposed for filtering laser data, which is closely related to the erosion operator used for mathematical grey scale morphology, based on height differences in a representative training dataset, filter functions are derived that either preserve important terrain characteristics or minimise the number of classification errors.