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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLII-5/W1
https://doi.org/10.5194/isprs-archives-XLII-5-W1-275-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/isprs-archives-XLII-5-W1-275-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
15 May 2017
CONSERVATION-ORIENTED HBIM. THE BIMEXPLORER WEB TOOL
R. Quattrini
Universtitá Politecnica delle Marche, Dipartimento di Ingegneria Civile, Edile e dell’ Architettura Via Brecce Bianche, 60100, Ancona, Italy
R. Pierdicca
Universtitá Politecnica delle Marche, Dipartimento di Ingegneria Civile, Edile e dell’ Architettura Via Brecce Bianche, 60100, Ancona, Italy
C. Morbidoni
Universtitá Politecnica delle Marche, Dipartimento di Ingegneria dell’ Informazione Via Brecce Bianche, 60100, Ancona, Italy
E. S. Malinverni
Universtitá Politecnica delle Marche, Dipartimento di Ingegneria Civile, Edile e dell’ Architettura Via Brecce Bianche, 60100, 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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ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 317–324, https://doi.org/10.5194/isprs-annals-V-2-2022-317-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-317-2022, 2022
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