Dr. Timm Schoening
Dr. Timm Schoening
Bestätigte E-Mail-Adresse bei geomar.de
Zitiert von
Zitiert von
Semi-automated image analysis for the assessment of megafaunal densities at the Arctic deep-sea observatory HAUSGARTEN
T Schoening, M Bergmann, J Ontrup, J Taylor, J Dannheim, J Gutt, ...
PloS one 7 (6), e38179, 2012
Biigle 2.0-browsing and annotating large marine image collections
D Langenkämper, M Zurowietz, T Schoening, TW Nattkemper
Frontiers in Marine Science 4, 83, 2017
DeepSurveyCam—a deep ocean optical mapping system
T Kwasnitschka, K Köser, J Sticklus, M Rothenbeck, T Weiß, E Wenzlaff, ...
Sensors 16 (2), 164, 2016
Current and future trends in marine image annotation software
JN Gomes-Pereira, V Auger, K Beisiegel, R Benjamin, M Bergmann, ...
Progress in Oceanography 149, 106-120, 2016
Perspectives in visual imaging for marine biology and ecology: from acquisition to understanding
JM Durden, T Schoening, F Althaus, A Friedman, R Garcia, AG Glover, ...
Oceanography and marine biology: an annual review 54, 1-72, 2016
Megafaunal variation in the abyssal landscape of the Clarion Clipperton Zone
E Simon-Lledó, BJ Bett, VAI Huvenne, T Schoening, NMA Benoist, ...
Progress in oceanography 170, 119-133, 2019
Comparison of image annotation data generated by multiple investigators for benthic ecology
JM Durden, BJ Bett, T Schoening, KJ Morris, TW Nattkemper, HA Ruhl
Marine Ecology Progress Series 552, 61-70, 2016
Fully automated image segmentation for benthic resource assessment of poly-metallic nodules
T Schoening, T Kuhn, DOB Jones, E Simon-Lledo, TW Nattkemper
Methods in Oceanography 15, 78-89, 2016
Ecology of a polymetallic nodule occurrence gradient: Implications for deep‐sea mining
E Simon‐Lledó, BJ Bett, VAI Huvenne, T Schoening, NMA Benoist, ...
Limnology and Oceanography 64 (5), 1883-1894, 2019
RecoMIA—Recommendations for marine image annotation: Lessons learned and future directions
T Schoening, J Osterloff, TW Nattkemper
Frontiers in Marine Science 3, 59, 2016
Understanding Mn-nodule distribution and evaluation of related deep-sea mining impacts using AUV-based hydroacoustic and optical data
A Peukert, T Schoening, E Alevizos, K Köser, T Kwasnitschka, J Greinert
Biogeosciences (BG) 15 (8), 2525-2549, 2018
Microhabitat and shrimp abundance within a Norwegian cold-water coral ecosystem
A Purser, J Ontrup, T Schoening, L Thomsen, R Tong, V Unnithan, ...
Biogeosciences (BG) 10 (9), 5779-5791, 2013
Biological effects 26 years after simulated deep-sea mining
E Simon-Lledó, BJ Bett, VAI Huvenne, K Köser, T Schoening, J Greinert, ...
Scientific reports 9 (1), 1-13, 2019
Compact-morphology-based poly-metallic nodule delineation
T Schoening, DOB Jones, J Greinert
Scientific reports 7 (1), 1-12, 2017
Quantitative mapping and predictive modeling of Mn nodules' distribution from hydroacoustic and optical AUV data linked by random forests machine learning
IZ Gazis, T Schoening, E Alevizos, J Greinert
Biogeosciences (BG) 15 (23), 7347-7377, 2018
DELPHI—fast and adaptive computational laser point detection and visual footprint quantification for arbitrary underwater image collections
T Schoening, T Kuhn, M Bergmann, TW Nattkemper
Frontiers in Marine Science 2, 20, 2015
Report on the marine imaging workshop 2017
T Schoening, J Durden, I Preuss, A Branzan Albu, A Purser, B De Smet, ...
Research Ideas and Outcomes 3, e13820, 2017
Rapid image processing and classification in underwater exploration using advanced high performance computing
T Schoening, D Langenkämper, B Steinbrink, D Brün, TW Nattkemper
OCEANS 2015-MTS/IEEE Washington, 1-5, 2015
Estimation of poly-metallic nodule coverage in benthic images
T Schoening, T Kuhn, TW Nattkemper
Proc. of the 41st Conference of the Underwater Mining Institute (UMI), 2012
Automated detection in benthic images for megafauna classification and marine resource exploration: supervised and unsupervised methods for classification and regression tasks …
T Schoening
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