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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume X-3-2024
https://doi.org/10.5194/isprs-annals-X-3-2024-61-2024
© Author(s) 2024. 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-annals-X-3-2024-61-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
04 Nov 2024
| 04 Nov 2024
Comparing the accuracy of 3D urban olive tree models detected by smartphone using LiDAR sensor, photogrammetry and NeRF: a case study of ’Ascolana Tenera’ in Italy
Stefano Chiappini
Department of Agricultural, Food and Environmental Sciences (D3A), Via Brecce Bianche, Ancona, 60131, Italy
Mattia Balestra
Department of Agricultural, Food and Environmental Sciences (D3A), Via Brecce Bianche, Ancona, 60131, Italy
Federico Giulioni
Department of Agricultural, Food and Environmental Sciences (D3A), Via Brecce Bianche, Ancona, 60131, Italy
Ernesto Marcheggiani
Department of Agricultural, Food and Environmental Sciences (D3A), Via Brecce Bianche, Ancona, 60131, Italy
Eva Savina Malinverni
Department of Civil, Building Engineering and Architecture (DICEA), Via Brecce Bianche, Ancona, 60131, Italy
Roberto Pierdicca
Department of Civil, Building Engineering and Architecture (DICEA), Via Brecce Bianche, Ancona, 60131, 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.
M. Balestra, S. Chiappini, A. Vitali, E. Tonelli, F. Malandra, A. Galli, C. Urbinati, E. S. Malinverni, and R. Pierdicca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 833–839, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-833-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-833-2022, 2022
D. Abate, M. Paolanti, R. Pierdicca, A. Lampropoulos, K. Toumbas, A. Agapiou, S. Vergis, E. Malinverni, K. Petrides, A. Felicetti, and P. Zingaretti
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 729–736, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-729-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-729-2022, 2022
F. Di Stefano, M. Cuevas-Gonzalez, G. Luzi, and E. S. Malinverni
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 263–270, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-263-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-263-2022, 2022
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
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
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