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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIX-B2-2026
https://doi.org/10.5194/isprs-archives-XLIX-B2-2026-1397-2026
© 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-B2-2026-1397-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
23 Jul 2026
| 23 Jul 2026
Automatic Extraction and Multi-Class Instance Segmentation of Rural Road Networks from Orthoimagery using YOLOv11 and SAHI Sliced Inference for Cadastral Update
Marsia Sanità
Department of Civil, Building and Architecture, Marche Polytechnic University, 60131 Ancona, Italy
Lindo Nepi
Department of Information Engineering (DII), Marche Polytechnic University, 60131 Ancona, Italy
Eva Savina Malinverni
Department of Civil, Building and Architecture, Marche Polytechnic University, 60131 Ancona, Italy
Adriano Mancini
Department of Information Engineering (DII), Marche Polytechnic University, 60131 Ancona, Italy
Roberto Pierdicca
Department of Civil, Building and Architecture, Marche Polytechnic University, 60131 Ancona, Italy
Artur Warchoł
Kielce University of Technology, Kielce, Poland
Monika Balawejder
PANS State University of Applied Sciences in Jaroslaw, Poland
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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
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F. Di Stefano, M. Cuevas-Gonzalez, G. Luzi, and E. S. Malinverni
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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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