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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-1/W1-2023
https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-131-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-131-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
25 May 2023
| 25 May 2023
POINT CLOUD CLASSIFICATION OF AN URBAN ENVIRONMENT USING A SEMI-AUTOMATIC APPROACH
F. Di Stefano
DICEA, Facoltà di Ingegneria, Università Politecnica delle Marche, 60131 Ancona, Italy
R. Pierdicca
DICEA, Facoltà di Ingegneria, Università Politecnica delle Marche, 60131 Ancona, Italy
E. S. Malinverni
DICEA, Facoltà di Ingegneria, Università Politecnica delle Marche, 60131 Ancona, Italy
A. Corneli
DICEA, Facoltà di Ingegneria, Università Politecnica delle Marche, 60131 Ancona, Italy
B. Naticchia
DICEA, Facoltà di Ingegneria, Università Politecnica delle Marche, 60131 Ancona, Italy
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Short summary
Short summary
For the processing of geomatics data, artificial intelligence (AI) offers overwhelming opportunities. The integration of AI approaches in geomatics has developed into the concept of geospatial artificial intelligence (GeoAI), which is a new paradigm for geographic knowledge discovery and beyond. This contribution outlines AI-based techniques for analysing and interpreting complex geomatics data. How AI approaches have been exploited for the interpretation of geomatic data is explained.
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