Peer Reviewed Journal Papers

Schmohl, S.; Narváez Vallejo, A. & Soergel, U. [2022]
Individual Tree Detection in Urban ALS Point Clouds with 3D Convolutional Networks. Remote Sens. 2022, 14(6), 1317.
DOI: 10.3390/rs14061317

Koelle, M.; Laupheimer, D.; Schmohl, S.; Haala, N.; Rottensteiner, F.; Wegner, J.D. & Ledoux, H. [2021]
The Hessigheim 3D (H3D) benchmark on semantic segmentation of high-resolution 3D point clouds and textured meshes from UAV LiDAR and Multi-View-Stereo. ISPRS Open Journal of Photogrammetry and Remote Sensing, 1, 2021.
DOI: 10.1016/j.ophoto.2021.100001

Schmohl, S.; Tutzauer, P. & Haala, N. [2020]
Stuttgart City Walk: A Case Study on Visualizing Textured DSM Meshes for the General Public Using Virtual Reality. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science volume 88, pages 147–154 (2020).
DOI: 10.1007/s41064-020-00106-z

Winiwarter, L.; Mandlburger, G.; Schmohl, S. & Pfeifer, N. [2019]
Classification of ALS Point Clouds Using End-to-End Deep Learning. PFG -- Journal of Photogrammetry, Remote Sensing and Geoinformation Science, Aug 2019, pp. 1-16.
DOI: 10.1007/s41064-019-00073-0

Peer Reviewed Conference Papers

Koelle, M.; Walter, V.; Schmohl, S.; Soergel U. [2021]
Remembering Both the Machine and the Crowd When Sampling Points: Active Learning for Semantic Segmentation of ALS Point Clouds. In: Del Bimbo A. et al. (eds) Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12667. Springer, Cham.
DOI: 10.1007/978-3-030-68787-8_37

Schmohl, S.; Koelle, M.; Frolow, R.; Soergel, U. [2021]
Towards Urban Tree Recognition in Airborne Point Clouds with Deep 3D Single-Shot Detectors. In: Del Bimbo A. et al. (eds) Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science, vol 12667. Springer, Cham.
DOI: 10.1007/978-3-030-68787-8_38

Koelle, M.; Walter, V.; Schmohl, S. & Soergel, U. [2020]
Evaluierung der Leistungsfähigkeit der Crowd für das Labeln von 3D-Punktwolken im Kontext von Active Learning. 40. Wissenschaftlich-Technische Jahrestagung der DGPF in Stuttgart – Publikationen der DGPF, Band 29, 2020, pp. 299-311.
URL: https://www.dgpf.de/src/tagung/jt2020/proceedings/proceedings/papers/38_DGPF2020_Koelle_et_al.pdf

Budde, L. E.; Schmohl, S. & Soergel, U. [2020]
Unsicherheitsauswertung von semantischer Segmentierung mittels Neuronaler Netze. 40. Wissenschaftlich-Technische Jahrestagung der DGPF in Stuttgart – Publikationen der DGPF, Band 29, 2020, pp. 280-289.
URL: https://www.dgpf.de/src/tagung/jt2020/proceedings/proceedings/papers/35_DGPF2020_Budde_et_al.pdf

Koelle, M.; Walter, V.; Schmohl, S. & Soergel, U. [2020]
Hybrid Acquisition of High Quality Training Data for Segmentation of 3D Point Clouds Using Crowd-based Active Learning. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 501-508, 2020.
DOI: 10.5194/isprs-annals-V-2-2020-501-2020

Schmohl, S. & Soergel, U. [2019]
Submanifold Sparse Convolutional Networks for Semantic Segmentation of Large-Scale ALS Point Clouds. Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 77-84.
DOI: 10.5194/isprs-annals-IV-2-W5-77-2019

Non-reviewed Conference Papers

Schmohl, S. & Soergel, U. [2019]
ALS Klassifizierung mit Submanifold Sparse Convolutional Networks. Dreiländertagung der DGPF, der OVG und der SGPF in Wien, Österreich – Publikationen der DGPF, Band 28, 2019, pp. 111-122.
URL: https://www.dgpf.de/src/tagung/jt2019/proceedings/proceedings/papers/23_3LT2019_Schmohl_Soergel.pdf

Cefalu, A.; Haala, N.; Schmohl, S., Neumann, I. & Genz, T. [2017]
A Mobile Multi-Sensor Platform for Building Reconstruction Integrating Terrestrial and Autonomous UAV-Based Close Range Data Acquisition. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W6, pp. 63-70.
DOI: 10.5194/isprs-archives-XLII-2-W6-63-2017