Mitarbeiter

Dr. Timm Schoening

RD 2: Marine Biogeochemistry, RU: Marine Geosystems
DeepSea Monitoring group (50%)

IDCC (Information-, Data and Computing Centre)
Research Data Management team (25%)

Technology and Logistics Center (25%)

Office:
Room: 8A-210 (DSM) / 01-110 (FDM)
Phone: +49 431 600-2320
Email: tschoening(at)geomar.de

Address:
Wischhofstraße 1-3
D-24148 Kiel

Research & Education

  • Since 2020 Researcher at GEOMAR
    in the DeepSea Monitoring Group. H2020 project "iAtlantic".
  • Since 2020 Research data manager for ROV/AUV data workflows at GEOMAR
  • 2019 - 2020 PostDoc at GEOMAR
    in the DeepSea Monitoring Group (Prof. Dr. Jens Greinert, Marine Geosystems). Automated Image Analysis in the framework of the JPIOceans “Mining Impact 2” Project.
  • 2018 - 2019 PostDoc in the Future Ocean excellence cluster
    at the GEOMAR Helmholtz-Centre for Ocean Research Kiel. Rapid off-shore Analysis of Marine Imagery
  • 2018 PostDoc at Okeanos, University of the Azores, Horta, Portugal
    (DFG Research Fellowship). Exploiting 3D information for an automated semantic analysis of underwater images
  • 2015 - 2017 PostDoc at the GEOMAR
    in the DeepSea Monitoring Group (Prof. Dr. Jens Greinert). Automated Image Analysis in the framework of the JPIOceans “EcoMining” Project.
  • 2015 Ph.D. in Computer Science:
    "Automated detection in benthic images for megafauna classification and marine resource exploration: supervised and unsupervised methods for classification and regression tasks in benthic images with efficient integration of expert knowledge" (Bielefeld University)
  • 2010 M.Sc. in Computer Science in the Natural Sciences:
    "Towards Improved Epilepsia Diagnosis by Unsupervised Segmentation of Neuropathology Tissue Sections using Ripley's-L Features" (Bielefeld University)
  • 2008 B.Sc. in Computer Science in the Natural Sciences:
    "Web 2.0 techniques for exploratory image database analysis" (Bielefeld University)

Cruises

  • PI for RV Maria S Merian MSM96 (2020): Emden - Emden; Metal geochemistry meets machine learning
  • RV Sonne SO268-1&2 (2019): Manzanillo – Vancouver; Station Planning, AUV Operations, Optical and Sensor Network Data Management, Automated Image Analysis, Manganese Nodule Measurements from BoxCores, Deployment Management in GIS
  • PI for RV Poseidon cruise POS526 “SeaSOM” (2018): Bergen – Kiel; Semi-autonomous optical monitoring technologies for bubble release sites and cold water coral reefs
  • MIDAS Pelagia cruise (2016): Faial – Faial; ROV and towed camera operations, data management and imagery analysis
  • RV Sonne SO242-1 (2015): Guayaquil – Guayaquil; AUV operation, image analysis and annotation
  • RV Sonne SO239 (2015): Panama – Manzanillo; AUV operation, image analysis and annotation
  • RV Sonne Testcruise (2014): Kiel – Gran Canaria; ROV video analysis and annotation

Selected Publications

Peer-reviewed:

  • Daniel OB Jones et al. Environment, ecology, and potential effectiveness of an area protected from deep-sea mining (Clarion Clipperton Zone, abyssal Pacific). Progress in Oceanography
    https://doi.org/10.1016/j.pocean.2021.102653
  • Timm Schoening, Autun Purser et al. Megafauna community assessment of polymetallic-nodule fields with cameras: platform and methodology comparison. Biogeosciences
    https://doi.org/10.5194/bg-17-3115-2020
  • Timm Schoening. SHiPCC—A Sea-going High-Performance Compute Cluster for Image Analysis. Frontiers in Marine Science
    https://doi.org/10.3389/fmars.2019.00736
  • Timm Schoening, Kevin Köser, Jens Greinert. An acquisition, curation and management workflow for sustainable, terabyte-scale marine image analysis. Scientific Data
    https://doi.org/10.1038/sdata.2018.181
  • Hongbo Liu, Jan Sticklus, Kevin Köser, Henk-Jan Hoving, Hong Song, Ying Chen, Jens Greinert, Timm Schoening. TuLUMIS - A tunable LED-based underwater multispectral imaging system. Optics Express
  • Peukert, A., Schoening, T., Alevizos, E., Köser, K., Kwasnitschka, T., & Greinert, J. Understanding Mn-nodule distribution and related deep-sea mining impacts using AUV-based hydroacoustic sensing and optical observations. Biogeosciences, 1-33.
  • Timm Schoening, Daniel Jones, Jens Greinert Compact Morphology based Delineation of Poly-Metallic Nodules (Nature Scientific Reports)
    https://doi.org/10.1038/s41598-017-13335-x
  • Timm Schoening, Jonas Osterloff, Tim W Nattkemper RecoMIA - Recommendations for Marine Image Annotation: Lessons Learned and Future Directions (Frontiers in Marine Science)
    https://doi.org/10.3389/fmars.2016.00059
  • Timm Schoening, Thomas Kuhn, Melanie Bergmann, Tim W Nattkemper DELPHI - fast and adaptive computational laser point detection and visual footprint quantification for arbitrary underwater image collections (Frontiers in Marine Science)
    https://doi.org/10.3389/fmars.2015.00020
  • Timm Schoening, Melanie Bergmann, Jörg Ontrup, et al. Semi-Automated Image Analysis for the Assessment of Megafaunal Densities at the Arctic Deep-Sea Observatory HAUSGARTEN (PLoS ONE)
    https://doi.org/10.1371/journal.pone.0038179

Data:

Other:

  • Timm Schoening et al. „Report on the Marine Imaging Workshop 2017” (Research Ideas and Outcomes)
    https://doi.org/10.3897/rio.3.e13820
  • Jen Durden, Timm Schoening et al. Visual imaging for marine biological and ecological research (Oceanography and Marine Biology: An Annual Review)

Awards

  • Briese Research PhD Award for Marine Science (2017)

Grants

  • "FDO-5DI" project Co-Proponent (HMC project call 2020)
  • "iAtlantic" project Co-Proponent (EU H2020)
  • Exploiting 3D information for an automated semantic analysis of underwater images” (DFG Research Fellowship)
  • Rapid, offshore Analysis of Marine Imagery” (Future Ocean Excellence Cluster – PostDoc Project Call)
  • General Purpose Underwater Spectral Imaging” (Future Ocean Excellence Cluster – Investment Call, proposal supporter)
  • Multi-Spectral Image Capture” (GEOMAR Seed Funding)

Practical Experience

  • Co-host of the 2019 “Image Analysis Days Schleswig-Holstein”
  • Chair of the scientific and local organizing committees to conduct the second Marine Imaging Workshop (held in February 2017 at GEOMAR)
  • Co-chair of the scientific organizing committee that initiated and conducted the first Marine Imaging Workshop (held in April 2014 at the National Oceanographic Centre, UK) with three days of poster and technical presentations of 100 international participants.
  • Exchange semester to visit the Bioimage Analysis Lab at the University of Warwick, UK

Research interests

My work aims to monitor the deep-sea with methods of pattern recognition and image processing. The focus lies on the exploration of large archives of 2D and 3D images of benthic and pelagic images.

Monitoring poly-metallic nodule abundance

In cooperation with the JPIO Partners, BGR and Saltation Marine IT, the amount, size and finally mass of polymetallic nodules lying on the seafloor is to be estimated. To monitor the minerals as well as the diverse biology in this sensitive habitat, precise measurements are required. This includes detections of the nodules as well as the biology.

The first part of this project was targeted to derive the percentage of the seafloor covered in nodules. Subsequently more detailed segmentation yielded single nodule sizes, and, combined with automatically detected laser markers, the relative size of the nodules in cm.

Automated detection of benthic mega fauna

One of the major projects, is the automated detection of mega fauna (as well as flora and Lebensspuren created by those animals). Images are provided by collaboration partners at the Alfred Wegener Institute and NOC Southampton. One result is an automated detection system (called iSIS, intelligent screening of image series) that incorporates the expertise of the biologists while keeping their input as small as possible. Parameters for different steps of the system are derived from a fully-annotated workshop transect. As the central part, supervised machine-learning is applied by Support Vector Machines.

Manual image annotation

A basis for the application of supervised and semi-supervised machine learning algorithms is a training set of reference samples. To obtain such a reference set, image annotation is required which was facilitated in different ways. Initially, whole images can be annotated using BIIGLE.

Web based visualization tools

A wide range of research focused visualizations tools has been implemented. These mostly facilitate link and brush interfaces to fuse representations of complex feature based descriptions with images or plots.