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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIX-B4-2026
https://doi.org/10.5194/isprs-archives-XLIX-B4-2026-133-2026
© 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-XLIX-B4-2026-133-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
04 Aug 2026
| 04 Aug 2026
High-resolution land cover mapping with GeoAI: instance segmentation for land cover analysis
Federica Gerla
Università Iuav di Venezia, CIRCE Lab, Venice, Italy
Sapienza Università di Roma, DICEA, Rome, Italy
Lindo Nepi
Università Politecnica delle Marche, DII, Ancona, Italy
Mihnea Cățeanu
Transilvania University of Bras, ov, Faculty of Silviculture and Forest Engineering, Bras, ov, Romania
Technical University of Civil Engineering, Faculty of Geodesy, Bucharest, Romania
Francesca Ferroni
Sapienza Università di Roma, Department of Environmental Biology, Rome, Italy
FAO, Mountain Partnership Secretariat, Rome, Italy
Khew Ee Hung
Hokkaido University, School of Agriculture, Japan
Krystian Mocny
Institute of Soil Science and Plant Cultivation, Department of Geomatics, Puławy, Poland
Shahla Yadollahi
Liège University, Environment Campus, Arlon, Belgium
Caterina Balletti
Università Iuav di Venezia, CIRCE Lab, Venice, Italy
Roberto Pierdicca
Università Politecnica delle Marche, DICEA, Ancona, Italy
Mattia Balestra
Università Politecnica delle Marche, DICEA, Ancona, Italy
Università Politecnica delle Marche, D3A, 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
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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
C. Balletti, F. Guerra, C. Meneghello, and G. Romanato
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