Koelle, M.; Walter, V. & Soergel, U. [2024]
Building a Fully-Automatized Active Learning Framework for the Semantic Segmentation of Geospatial 3D Point Clouds. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science.
DOI: 10.1007/s41064-024-00281-3

Walter, V.; Koelle, M. & Collmar, D. [2022]
Measuring the Wisdom of the Crowd: How Many is Enough?. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2022.
DOI: 10.1007/s41064-022-00202-2

Haala, N.; Koelle, M.; Cramer, M.; Laupheimer, D. & Zimmermann, F. [2022]
Hybrid georeferencing of images and LiDAR data for UAV-based point cloud collection at millimetre accuracy. ISPRS Open Journal of Photogrammetry and Remote Sensing, 2022, 100014, ISSN 2667-3932.
DOI: 10.1016/j.ophoto.2022.100014

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

Mandlburger, G.; Koelle, M.; Nuebel, H. & Soergel, U. [2021]
BathyNet: A Deep Neural Network for Water Depth Mapping from Multispectral Aerial Images. PFG.
DOI: 10.1007/s41064-021-00142-3

Cramer, M.; Koelle, M.; Haala, N. & Havel, P. [2020]
Flächenhaftes UAV-Monitoring: das Hessigheim-Projekt. Allgemeine Vermessungsnachrichten (avn) 03/2020, pp. 107-117 (2020).

Haala, N.; Koelle, M. & Laupheimer, D. [2020]
Integrating UAV-based LiDAR and Photogrammetry. GIM International May 2020, Volume 34, Issue 3, pp. 10-13 (2020).
URL: https://www.gim-international.com/content/article/integrating-uav-based-lidar-and-photogrammetry

Koelle, M.; Walter, V.; Schmohl, S. & Soergel, U. [2023]
LEARNING ON THE EDGE: BENCHMARKING ACTIVE LEARNING FOR THE SEMANTIC SEGMENTATION OF ALS POINT CLOUDS. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1/W1-2023, 945–952.
DOI: 10.5194/isprs-annals-X-1-W1-2023-945-2023

Collmar, D.; Walter, V.; Koelle, M. & Soergel, U. [2023]
FROM MULTIPLE POLYGONS TO SINGLE GEOMETRY: OPTIMIZATION OF POLYGON INTEGRATION FOR CROWDSOURCED DATA. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1/W1-2023, 159–166.
DOI: 10.5194/isprs-annals-X-1-W1-2023-159-2023

Walter, V.; Koelle, M. & Collmar, D. [2022]
A Gamification Approach for the Improvement of Paid Crowd-based Labelling of Geospatial Data. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2022, 113–120, 2022.
DOI: 10.5194/isprs-annals-V-4-2022-113-2022

Koelle, M.; Walter, V. & Soergel, U. [2022]
Learning from the Past: Crowd-driven Active Transfer Learning for Semantic Segmentation of Multi-Temporal 3D Point Clouds. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 259–266, 2022.
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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

Walter, V.; Koelle, M.; Collmar, D. & Zhang, Y. [2021]
A Two-Level Approach for the Crowd-Based Collection of Vehicles from 3D Point Clouds. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2021, 97–104, 2021.
DOI: 10.5194/isprs-annals-V-4-2021-97-2021

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.
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Koelle, M.; Laupheimer, D.; Walter, V.; Haala, N. & Soergel, U. [2021]
Which 3D Data Representation Does the Crowd Like Best? Crowd-Based Active Learning for Coupled Semantic Segmentation of Point Clouds and Textured Meshes. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 93–100, 2021.
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Koelle, M.; Walter, V.; Shiller, I.; Soergel U. [2021]
CATEGORISE: An Automated Framework for Utilizing the Workforce of the Crowd for Semantic Segmentation of 3D Point Clouds. In: Bauckhage C. et al. (eds) Pattern Recognition. GCPR 2021. Lecture Notes in Computer Science, vol 13024. Springer, Cham.
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Nuebel, H.; Mandlburger, G., Koelle, M. & Soergel, U. [2020]
Bathymetrieableitung aus multispektralen Luftbildern über Convolutional Neural Networks. 40. Wissenschaftlich-Technische Jahrestagung der DGPF in Stuttgart – Publikationen der DGPF, Band 29, 2020, pp. 156-171.
URL: https://www.dgpf.de/src/tagung/jt2020/proceedings/proceedings/papers/19_DGPF2020_Nuebel_et_al.pdf

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

Haala, N.; Koelle, M.; Cramer, M.; Laupheimer, D.; Mandlburger, G. & Glira, P. [2020]
Hybrid Georeferencing, Enhancement and Classification of Ultra-High Resolution UAV LiDAR and Image Point Clouds for Monitorig Applications. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 727–734, 2020.
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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.
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Walter, V.; Koelle, M. & Yin, Y. [2020]
Evaluation and Optimisation of Crowd-based Collection of Trees from 3D Point Clouds. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 49-56, 2020.
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Hadas, E.; Koelle, M.; Karpina, M. & Borkowski, A. [2020]
Identification of Peach Tree Trunks from Laser Scanning Data Obtained with Small Unmanned Aerial System. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 735–740, 2020.
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Yin, Y.; Koelle, M. & Walter, V. [2020]
Entwicklung und Evaluierung eines Web-Tools zur bezahlten crowd-basierten Erfassung von Bäumen aus 3D-Punktwolken. 40. Wissenschaftlich-Technische Jahrestagung der DGPF in Stuttgart – Publikationen der DGPF, Band 29, 2020, pp. 478-487.
URL: https://www.dgpf.de/src/tagung/jt2020/proceedings/proceedings/papers/71_KKNP_DGPF2020_Yin_et_al.pdf

Koelle, M.; Laupheimer, D. & Haala, N. [2019]
Klassifikation hochaufgelöster LiDAR- und MVS-Punktwolken zu Monitoringzwecken. Dreiländertagung der DGPF, der OVG und der SGPF in Wien, Österreich – Publikationen der DGPF, Band 28, 2019, pp. 692-701.
URL: https://www.dgpf.de/src/tagung/jt2019/proceedings/proceedings/papers/07_KKNP_3LT19_Koelle_et_al.pdf

Haala, N.; Mandlburger, G.; Cramer, M. Laupheimer, D.; Koelle, M. [2019]
Kombinierte Analyse hochpräziser Punktwolken aus UAV-Photogrammetrie und -Laserscanning im Hinblick auf Setzungsmessungen. 20. Internationale Geodätische Woche Obergurgl 2019, 10 p.
URL: https://www.uibk.ac.at/geometrie-vermessung/gruppe_vermessung_und_geoinformation/geodaetische_wochen/obergurgl_2019/beitraege/vo_haala.pdf