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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-2/W12-2026
https://doi.org/10.5194/isprs-archives-XLVIII-2-W12-2026-17-2026
© Author(s) 2026. 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-2-W12-2026-17-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
12 Feb 2026
| 12 Feb 2026
GARF-CH: Generalizable Flow-Matched 3D Reassembly of Fragmented Cultural Heritage Point Clouds
Emanuele Balloni
Department of Construction, Civil Engineering and Architecture (DICEA), Universit`a Politecnica delle Marche, Via Brecce Bianche, 12, 60131, Ancona, Italy
Marina Paolanti
Department of Political Sciences, Communication and International Relations, University of Macerata, Via Don Minzoni, 22A, 62100, Macerata, Italy
Emanuele Frontoni
Department of Political Sciences, Communication and International Relations, University of Macerata, Via Don Minzoni, 22A, 62100, Macerata, Italy
Stefano Mereu
Industrial Robotics Facility, Italian Institute of Technology, Via Greto di Cornigliano, 6/R, 16152, Genoa, Italy
Ferdinando Cannella
Industrial Robotics Facility, Italian Institute of Technology, Via Greto di Cornigliano, 6/R, 16152, Genoa, Italy
Roberto Pierdicca
Department of Construction, Civil Engineering and Architecture (DICEA), Universit`a Politecnica delle Marche, Via Brecce Bianche, 12, 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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M. Balestra, S. Chiappini, A. Vitali, E. Tonelli, F. Malandra, A. Galli, C. Urbinati, E. S. Malinverni, and R. Pierdicca
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A. Murtiyoso, F. Matrone, M. Martini, A. Lingua, P. Grussenmeyer, and R. Pierdicca
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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