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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-G-2025
https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1315-2025
© Author(s) 2025. 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-G-2025-1315-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
31 Jul 2025
| 31 Jul 2025
AI4EO hyperview challenge: combination of machine learning methods on hyperspectral images to predict the soil parameters
Marsia Sanità
Dipartimento di Ingegneria Civile, Edile e dell’Architettura (DICEA), Università Politecnica delle Marche, 60100 Ancona, Italy
Eva Savina Malinverni
Dipartimento di Ingegneria Civile, Edile e dell’Architettura (DICEA), Università Politecnica delle Marche, 60100 Ancona, Italy
Roberto Pierdicca
Dipartimento di Ingegneria Civile, Edile e dell’Architettura (DICEA), Università Politecnica delle Marche, 60100 Ancona, Italy
Adriano Mancini
Department of Information Engineering (DII), Università Politecnica delle Marche, 60131, Ancona, Italy
Ewa Glowienka
Department of Photogrammetry, Remote Sensing, and Spatial Engineering, Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, AGH University of Krakow, 30-059 Krakow, Poland
Lindo Nepi
Department of Information Engineering (DII), 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.
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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