| 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 | 80 | 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 | 57 | 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 | 44 | 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 | 39 | 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 | 37 | 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 | 35 | 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 | 33 | 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 | 24 | 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 | 23 | 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 | 23 | 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 | 22 | 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 | 22 | 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 | 21 | 2019 |
| Compact-morphology-based poly-metallic nodule delineation T Schoening, DOB Jones, J Greinert Scientific reports 7 (1), 1-12, 2017 | 21 | 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 | 13 | 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 | 12 | 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 | 10 | 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 | 9 | 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 | 9 | 2012 |
| Automated detection in benthic images for megafauna classification and marine resource exploration: supervised and unsupervised methods for classification and regression tasks … T Schoening | 8 | 2015 |
