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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIV-4/W1-2020
https://doi.org/10.5194/isprs-archives-XLIV-4-W1-2020-95-2020
© Author(s) 2020. 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-XLIV-4-W1-2020-95-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
03 Sep 2020
| 03 Sep 2020
GRAPH CNN WITH RADIUS DISTANCE FOR SEMANTIC SEGMENTATION OF HISTORICAL BUILDINGS TLS POINT CLOUDS
C. Morbidoni
Universitá Politecnica delle Marche, Ancona, Italy
R. Pierdicca
Universitá Politecnica delle Marche, Ancona, Italy
R. Quattrini
Universitá Politecnica delle Marche, Ancona, Italy
E. Frontoni
Universitá Politecnica delle Marche, Ancona, Italy
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Short summary
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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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