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Norbert Pfeifer
Researcher at Vienna University of Technology
Publications - 281
Citations - 10086
Norbert Pfeifer is an academic researcher from Vienna University of Technology. The author has contributed to research in topics: Point cloud & Lidar. The author has an hindex of 49, co-authored 249 publications receiving 8855 citations. Previous affiliations of Norbert Pfeifer include University of Vienna & University of Innsbruck.
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Journal ArticleDOI
A Comparison of Deep Learning Methods for Airborne Lidar Point Clouds Classification
TL;DR: In this article, a comprehensive comparison among three state-of-the-art deep learning networks: PointNet++, SparseCNN, and KPConv, on two different ALS datasets is presented.
Book ChapterDOI
Laser scanning - a paradigm change in topographic data acquisition for natural hazard management
TL;DR: In this paper, the authors present the project "Determination of surface properties from laser scanning data" which addresses these demands by incorporating certain aspects of remote sensing in natural hazard management.
Journal ArticleDOI
Flugzeuggestütztes Laserscanning für ein operationelles Waldstrukturmonitoring
Reik Leiterer,Werner Mücke,Felix Morsdorf,Markus Hollaus,Norbert Pfeifer,Michael E. Schaepman +5 more
TL;DR: In this article, robuste Verfahren basierend auf flugzeuggestutzten Laserscanningdaten vor, um eine extraktion von forstwirtschaftlich and -wissenschaftlic relevanten Strukturinformationen zu ermoglichen.
Book ChapterDOI
Object detection in airborne laser scanning data - an integrative approach on object-based image and point cloud analysis
TL;DR: An integrative approach combining object- based image analysis and object-based point cloud analysis is applied to building detection in the raster domain followed by a 3D roof facet delineation and classification in the point cloud.
Journal ArticleDOI
A Clustering Framework for Monitoring Circadian Rhythm in Structural Dynamics in Plants From Terrestrial Laser Scanning Time Series.
Eetu Puttonen,Matti Lehtomäki,Paula Litkey,Roope Näsi,Ziyi Feng,Xinlian Liang,Samantha Wittke,Samantha Wittke,Miloš Pandžić,Teemu Hakala,Mika Karjalainen,Norbert Pfeifer +11 more
TL;DR: The results showed that the processing framework presented can capture a plant's circadian rhythm in crown and branches down to a spatial resolution of 1 cm, which is well within the acceptable range of TLS point cloud time series.