Bücher, Buchkapitel, Dissertationen

Soergel, Uwe; Wegner, Jan Dirk; Thiele, Antje [2016]
Fusion von optischen und Radardaten. In Band „Photogrammetrie und Fernerkundung“, Christian Heipke (Ed.) – Handbuch der Geodäsie, 6 Bände, Willi Freeden, Reiner Rummel (Eds.). Springer Berlin Heidelberg, ISBN 978-3-662-46900-2.
DOI: 10.1007/978-3-662-46900-2_54-1

Begutachtete Zeitschriftenartikel

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

Haala, N.; Zhang, W.; Joachim, L.; Skuddis, D.; Abolhasani, S.; Schwieger, V. & Soergel, U. [2023]
Zum Potenzial von SLAM-Verfahren für geodätische Echtzeit-Messaufgaben. Allgemeine Vermessungsnachrichten (avn), 163–172.
URL: https://gispoint.de/artikelarchiv/avn/2023/avn-ausgabe-052023/7879-zum-potenzial-von-slam-verfahren-fuer-geodaetische-echtzeit-messaufgaben.html

Burkhardt, M.; Gienger, A.; Joachim, L.; Haala, N.; Soergel, U., & Sawodny, O. [2023]
Data-based error compensation for georeferenced payload path tracking of automated tower cranes. Mechatronics, 94, 103028--.
DOI: 10.1016/j.mechatronics.2023.103028

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

Wang, J.; Gong, K.; Balz, T.; Haala, N.; Soergel, U.; Zhang, L. & Liao, M. [2022]
Radargrammetric DSM Generation by Semi-Global Matching and Evaluation of Penalty Functions. Remote Sens. 2022, 14, 1778.
DOI: 10.3390/rs14081778

Bill, R.; Blankenbach, J.; Breunig, M.; Haunert, J.-H,; Heipke, Ch.; Herle, S.; Maas, H.; Mayer, H.; Meng, L.; Rottensteiner, F.; Schiewe, J.; Sester, M.; Soergel, U.; Werner, M. [2022]
Geospatial Information Research: State of the Art, Case Studies and Future Perspectives. PFG (2022).
DOI: 10.1007/s41064-022-00217-9

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

Schneider, P. J. & Soergel, U. [2020]
Monitoring einer Schleuse mittels Persistent-Scatterer-Interferometrie. avn - allgemeine vermessungs-nachrichten, Vol. 3, 2020, pp. 118-123.
URL: https://gispoint.de/artikelarchiv/avn/2020/avn-ausgabe-032020/6493.html

Farghaly, Dalia; Urban, Brigitte; Soergel, Uwe & Elb, Emad [2019]
Differentiating forest types using TerraSAR–X spotlight images based on inferential statistics and multivariate analysis. Remote Sensing Applications: Society and Environment.
DOI: 10.1016/j.rsase.2019.100238

Gross, W.; Tuia, D.; Soergel, U. & Middelmann, W. [2019]
Nonlinear Feature Normalization for Hyperspectral Domain Adaptation and Mitigation of Nonlinear Effects. IEEE Transactions on Geoscience and Remote Sensing, Volume: 57 , Issue: 8 , Aug. 2019. pp. 5975-5990.
DOI: 10.1109/TGRS.2019.2903719

Gu, H.; Han, Y.; Yang, Y.; Li, H.; Liu, Z.; Soergel, U.; Blaschke, T. & Cui, S. [2018]
An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery. Remote Sens. 2018, 10, 590.
DOI: 10.3390/rs10040590

Kenduiywo, B. K.; Bargiel, D. & Soergel, U. [2018]
Crop-type mapping from a sequence of Sentinel 1 images. International Journal of Remote Sensing.
DOI: 10.1080/01431161.2018.1460503

Schilling, H.; Bulatov, D.; Niessner, R.; Middelmann W. and Soergel, U. [2018]
Detection of Vehicles in Multisensor Data via Multibranch Convolutional Neural Networks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
DOI: 10.1109/JSTARS.2018.2825099

Li, H.; Ghamisi, P.; Soergel, U.& Zhu, X.X [2018]
Hyperspectral and LiDAR Fusion Using Deep Three-Stream Convolutional Neural Networks. Remote Sens. 2018, 10, 1649.
DOI: 10.3390/rs10101649

Walter, V.& Soergel, U. [2018]
Results and Problems of paid Crowd-based geospatial Data Collection. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science. pp 1-11.
DOI: 10.1007/s41064-018-0058-z

Gu, H.; Li, H.; Yan, L.; Liu, Z.; Blaschke, T. & Soergel, U. [2017]
An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology. Remote Sens. 2017, 9, 329.
DOI: 10.3390/rs9040329

Kenduiywo, B. K.; Bargiel, D. & Soergel, U. [2017]
Higher Order Dynamic Conditional Random Fields Ensemble for Crop Type Classification in Radar Images. IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, Volume 55, Issue: 8, Aug. 2017, pp. 4638-4654.
DOI: 10.1109/TGRS.2017.2695326

Schack, L.; Soergel, U. & Heipke, C. [2017]
Assigning Persistent Scatterers of Regular Multi-Story Buildings to Optical Oblique Images. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, (), 1-8.
URL: http://link.springer.com/article/10.1007/s41064-017-0005-4
DOI: 10.1007/s41064-017-0005-4

Schunert, A. & Soergel, U. [2016]
Assignment of Persistent Scatterers to Buildings. In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, Volume 54, Issue 6, pp. 3116-3127.
DOI: 10.1109/TGRS.2015.2511583

Weitere Zeitschriftenartikel

Fritsch, D. & Soergel, U. [2023]
In memoriam Prof. Dr.-Ing. Dr. h.C. mult. Friedrich (Fritz) Ackermann 1929 – 2021. Geo-spatial Information Science.
DOI: 10.1080/10095020.2023.2231728

Begutachtete Tagungsbeiträge

Budde, Lina E.; Collmar, David; Soergel, Uwe; Iwaszczuk, Dorota [2024]
Investigating the Relationship between Image Quality and Crowdsourcing for Labeling. 44. Wissenschaftlich-Technische Jahrestagung der DGPF in Remagen – Publikationen der DGPF, Band 32, 2024, pp. 191-202.
URL: https://www.dgpf.de/src/tagung/jt2024/proceedings/paper/12_dgpf2024_Budde_et_al.pdf

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

Collmar, D.; Walter, V.; Koelle, M. Soergel, U. [2023]
A Two-Step Approach for the Acquisition of Individual Tree Outlines using Paid Crowdsourcing. 43. Wissenschaftlich-Technische Jahrestagung der DGPF in München – Publikationen der DGPF, Band 31, 2023.
URL: https://www.dgpf.de/src/tagung/jt2023/proceedings/paper/09_dgpf2023_Collmar_et_al.pdf
DOI: 10.24407/KXP:1841079707

Schneider, P. J.; Yang, C.-H.; Li, Y., Koppe, M.; Soergel, U.; Pakzad, K. & Rudolf, T. [2023]
DEVELOPMENT OF A WEB PLATFORM TO VISUALIZE PS-INSAR DATA IN A BUILDING INFORMATION MANAGEMENT SYSTEM. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1/W1-2023, 869–873.
DOI: 10.5194/isprs-annals-X-1-W1-2023-869-2023

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

Xiang, M.; Azimi, S.; Bahmanyar, R.; Soergel, U. & Reinartz, P. [2023]
VEHICLE OCCLUSION REMOVAL FROM SINGLE AERIAL IMAGES USING GENERATIVE ADVERSARIAL NETWORKS. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-1/W1-2023, 629–634.
DOI: 10.5194/isprs-annals-X-1-W1-2023-629-2023

Schneider, P. J. & Soergel, U. [2022]
MATCHING PERSISTENT SCATTERER CLUSTERS TO BUILDING ELEMENTS IN MESH REPRESENTATION. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. V-3-2022, 123--130.
DOI: 10.5194/isprs-annals-V-3-2022-123-2022

Joachim, L.; Zhang, W.; Haala, N. & Soergel, U. [2022]
Evaluation of the Quality of Real-time Mapping With Crane Cameras and Visual SLAM Algorithms. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 545-552, 2022.
DOI: 10.5194/isprs-archives-XLIII-B2-2022-545-2022

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.
DOI: 10.5194/isprs-annals-V-2-2022-259-2022

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.; 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.
DOI: 10.5194/isprs-annals-V-2-2021-93-2021

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.
DOI: 10.1007/978-3-030-92659-5_41

Schneider, P. J. & Soergel, U. [2021]
Clustering Persistent Scatterer Points Based on a Hybrid Distance Metric. In: Bauckhage C., Gall J., Schwing A. (eds) Pattern Recognition. DAGM GCPR 2021. Lecture Notes in Computer Science, vol 13024. Springer, Cham.
DOI: 10.3390/rs10111816

Schneider, P. J. & Soergel, U. [2021]
Segmentation of Buildings Based on High Resolution Persistent Scatterer Point Clouds. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 65–71.
DOI: 10.5194/isprs-annals-V-3-2021-65-2021

Schneider, P. J.; Khamis, R. & Soergel, U. [2020]
Extracting and Evaluating Clusters In DInSAR Deformation Data on Single Buildings. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 157–163.
DOI: 10.5194/isprs-annals-V-3-2020-157-2020

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

Bolz, T.; Schmitz, S.; Thiele, A.; Soergel, U. & Hinz, S. [2020]
Analysis of Airborne SAR and InSAR Data for Coastal Monitoring. 40. Wissenschaftlich-Technische Jahrestagung der DGPF in Stuttgart – Publikationen der DGPF, Band 29, 2020, pp. 457-461.
URL: https://www.dgpf.de/src/tagung/jt2020/proceedings/proceedings/papers/65_DGPF2020_Bolz_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

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

Schneider, P. J. & Soergel, U. [2020]
Monitoring einer Schleuse mittels satellitengestützter DInSAR-Techniken. 40. Wissenschaftlich-Technische Jahrestagung der DGPF in Stuttgart – Publikationen der DGPF, Band 29, 2020, pp. 448-456.
URL: https://www.dgpf.de/src/tagung/jt2020/proceedings/proceedings/papers/64_DGPF2020_Schneider_Soergel.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

Mandlburger, G.; Pfeifer, N. & Soergel, U. [2017]
Water Surface Reconstruction in Airborne Laser Bathymetry from Redundant Bed Observations. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W4, 123-130.
DOI: 10.5194/isprs-annals-IV-2-W4-123-2017

Yang, C.H. & Soergel, U. [2017]
Monitoring of Building Construction by 4D Change Detection Using Multi-temporal SAR Images. ISPRS Annals of The Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-1/W1, pp. 35-42 (ISPRS Hannover Workshop 2017, Hannover, Germany).
DOI: 10.5194/isprs-annals-IV-1-W1-35-2017

Kenduiywo, B. K.; Bargiel, D. & Soergel, U. [2016]
Crop Type Mapping from a Sequence of TerraSAR-X Images with Dynamic Conditional Random Fields. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, pp. 59-66.
DOI: 10.5194/isprs-annals-III-7-59-2016

Yang, C.-H.; Kenduiywo, B.K. & Soergel, U. [2016]
4D change detection based on persistent scatterer interferometry. Pattern Recogniton in Remote Sensing (PRRS), 2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing, Cancun, Mexico. 6p.
DOI: 10.1109/PRRS.2016.7867016

Schack, L.; Soergel, U. & Heipke, C. [2016]
Graph Matching for the Registration of Persistent Scatters to Optical Oblique Imagery. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, pp. 195-202.
DOI: 10.5194/isprs-annals-III-7-195-2016

Yang, C.-H.; Kenduiywo, B. K. & Soergel, U. [2016]
Change Detection Based on Persistent Scatterer Interferometry – A New Method of Monitoring Building Changes. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, pp. 243-250.
DOI: 10.5194/isprs-annals-III-7-243-2016

Weitere Tagungsbeiträge

Zhang, X.; Lin, D.; Xue, R. & Soergel, U. [2023]
TARGET-GUIDED LEARNING FOR RARE CLASS SEGMENTATION IN LARGE-SCALE URBAN POINT CLOUDS. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1/W2-2023, 1693–1698.
DOI: 10.5194/isprs-archives-XLVIII-1-W2-2023-1693-2023

Zhang, X., Xue, R., & Soergel, U. [2022]
A TWO-STAGE APPROACH FOR RARE CLASS SEGMENTATION IN LARGE-SCALE URBAN POINT CLOUDS. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 329-334.
DOI: 10.5194/isprs-archives-XLIII-B2-2022-329-2022

Xue, R., Zhang, X., & Soergel, U. [2022]
URBAN CLASSIFICATION BASED ON TOP-VIEW POINT CLOUD AND SAR IMAGE FUSION WITH SWIN TRANSFORMER. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 559-564.
DOI: 10.5194/isprs-archives-XLIII-B3-2022-559-2022

Joachim, L.; Ackermann, S.; Haala, N. & Soergel, U. [2021]
Using Crane Cameras for Workspace Mapping and Monitoring for an Autonomous Tower Crane. 7th Int. Conference on Machine Control & Guidance, 2021. ISBN: 978-3-00-071292-0, pp. 47 – 53.

Brockmann, H,.; Soergel, U.; Havel, P.; Röpel, L.; Cramer, M. & Schneider, P. J. [2020]
Multisensorales ingenieurgeodätisches Bauwerks- und -umfeldmonitoring. Dresdner Wasserbauliche Mitteilungen.
URL: https://hdl.handle.net/20.500.11970/107069

Yang, C. H.; Müterthies, A. & Soergel, U. [2019]
Workable Monitoring System Based on Spaceborne SAR Images for Mining Areas - Stings Development Project. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1951-1957.
DOI: 10.5194/isprs-archives-XLII-2-W13-1951-2019

Yang, C. H. & Soergel, U. [2019]
Evaluation of a PSI-based Change Detection Regarding Simulation, Comparison, and Application. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W13, 1959-1965.
DOI: 10.5194/isprs-archives-XLII-2-W13-1959-2019

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

Yang, C.H. & Soergel, U. [2017]
Urban Monitoring by 4D Change Detection Using Multi-temporal SAR Images. DGPF annual conference, Würzburg, Germany. Publikationen der DGPF, Band 26, 2017. pp. 233-244.
URL: http://www.dgpf.de/src/tagung/jt2017/proceedings/proceedings/papers/23_DGPF2017_Yang_Soergel.pdf

Schmidt, A.; Kruse, C.; Rottensteiner, F.; Soergel, U. & Heipke, C. [2016]
Network Detection in Raster Data Using Marked Point Processes. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, pp. 701-708.
DOI: 10.5194/isprs-archives-XLI-B3-701-2016

Niemeyer, J.; Rottensteiner, F.; Soergel, U. & Heipke, C. [2016]
Hierarchical Higher Order CRF for the Classification of Airborne Lidar Point Clouds in Urban Areas. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, pp. 655-662.
DOI: 10.5194/isprs-archives-XLI-B3-655-2016

Schack, L.; Soergel, U. & Heipke, C. [2016]
Graph matching for the registration of persistent scatterers to optical oblique imagery. Proceedings, Asian Conference on Remote Sensing, Colombo, 8p., on USB-Stick.

Balz, T.; Haala, N. & Soergel, U. [2005]
Combining LIDAR, InSAR, SAR and SAR-Simulation for Change Detection Applications in Cloud-prone Areas. In: 1st Intern. Symposium on Cloude-Prone and Rainy Areas Remote Sensing (CARRS), Hong-Kong, pp. 68-79.