HexDTM : a method of thinning LiDAR point clouds using cartographic thematic data classification

Published in:: Abstracts of the ICA
Vol. 7.2024 (02.09.2024), art. no. 61, p. 1-2
Volume:: 7.2024
Publication date:: 02.09.2024
URN:: urn:nbn:de:0307-20250908-000-7
DOI:: 10.5194/ica-abs-7-61-2024
Language:: English
Type of Resource:: Text
Extent:: 1 Online-Ressource (Seite 1-2, 1,38 MB) : 1 Illustration, 1 Textdatei (PDF)
Keywords:: Lidar^GND; classification; generalization; Klassifikation^GND; Generalisierung^GND; Kartografie^GND
DDC subject of DNB:: 520 Astronomy & allied sciences
URL:: https://doi.org/10.5194/ica-abs-7-61-2024
URL:: https://ica-abs.copernicus.org/articles/7/61/2024/
Link to Raw Object:: https://ica-abs.copernicus.org/articles/7/61/2024/ica-abs-7-61-2024.pdf
Hosting institution:: AGH University of Science and Technology [PL]
Hosting institution:: Gdańsk University of Technology (PG) [PL]
Hosting institution:: University of Twente (UT) [NL]
Hosting institution:: Internationale Forschungseinrichtungen
Statement of responsibility:: Adam Inglot, Paulo Raposo, Kristian Kozioł
Bibliographic notes:: Published in: Abstracts of the ICA, Volume 7, 2024 - European Cartographic Conference – EuroCarto 2024; 9–11 September 2024, TU Wien, Vienna, Austria; Editors: Georg Gartner, Florian Ledermann, and Andrea Binn | https://ica-abs.copernicus.org/articles/7/index.html