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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XI-2-2026
https://doi.org/10.5194/isprs-annals-XI-2-2026-293-2026
© 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-annals-XI-2-2026-293-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
03 Jul 2026
| 03 Jul 2026
GT-LOD3: LOD3 Semantic 3D Building Reconstruction Benchmark Dataset
Han Sae Kim
Lyles School of Civil and Construction Engineering, Purdue University, West Lafayette, IN, USA
Olaf Wysocki
CV4DT, Department of Engineering, University of Cambridge, Cambridge, United Kingdom
Ludwig Hoegner
Department of Geoinfomatics, Munich University of Applied Sciences, Munich, Germany
Ksenia Bittner
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
Joshua Carpenter
Department of Civil Engineering, The University of Akron, Akron, OH, USA
Friedrich Fraundorfer
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
Institute of Visual Computing, Graz University of Technology, Graz, Austria
Arpan Kusari
University of Michigan Transportation Research Institute, University of Michigan, Ann Arbor, MI, USA
Max Mehltretter
Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, Hannover, Germany
Franz Rottensteiner
Institute of Photogrammetry and GeoInformation, Leibniz University Hannover, Hannover, Germany
Anna Schadl
Faculty of Geoinformatics, Hochschule München University of Applied Sciences, Munich, Germany
Martin Weinmann
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
Jinha Jung
Lyles School of Civil and Construction Engineering, Purdue University, West Lafayette, IN, USA
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P. Trusheim, M. Mehltretter, F. Rottensteiner, and C. Heipke
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2022, 169–177, https://doi.org/10.5194/isprs-annals-V-1-2022-169-2022, https://doi.org/10.5194/isprs-annals-V-1-2022-169-2022, 2022
M. Mehltretter
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 69–78, https://doi.org/10.5194/isprs-annals-V-2-2022-69-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-69-2022, 2022
A. Kusari and W. Sun
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 127–134, https://doi.org/10.5194/isprs-annals-V-2-2022-127-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-127-2022, 2022
D. Wittich, F. Rottensteiner, M. Voelsen, C. Heipke, and S. Müller
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 307–315, https://doi.org/10.5194/isprs-annals-V-2-2022-307-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-307-2022, 2022
P. Schuegraf, J. Schnell, C. Henry, and K. Bittner
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 407–414, https://doi.org/10.5194/isprs-annals-V-2-2022-407-2022, https://doi.org/10.5194/isprs-annals-V-2-2022-407-2022, 2022
O. Wysocki, L. Hoegner, and U. Stilla
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2-W1-2022, 529–536, https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-529-2022, https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-529-2022, 2022
O. Wysocki, Y. Xu, and U. Stilla
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VIII-4-W2-2021, 45–52, https://doi.org/10.5194/isprs-annals-VIII-4-W2-2021-45-2021, https://doi.org/10.5194/isprs-annals-VIII-4-W2-2021-45-2021, 2021
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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