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CATAMI presentation from Australian Marine Science Association (AMSA) 2013 conference at the Gold Coast (11th July 2013). Link to screencase demo given on the day here: …

CATAMI presentation from Australian Marine Science Association (AMSA) 2013 conference at the Gold Coast (11th July 2013). Link to screencase demo given on the day here: here

Actual web application -

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  • 1. CATAMI (Collaborative and Annotation Tools for Analysis of Marine Imagery and video): Unlocking the potential of marine imagery Luke Edwards (presenter – iVEC / WAMSI) Mat Wyatt (iVEC / ANDS), Mark Gray (iVEC), Derrick Wong (iVEC)
  • 2. Challenges • Huge growth in underwater imagery Courtesy of IMOS Courtesy of SeaGIS Courtesy of UWA • Time intensive to annotate • Fragmented approach to • storage locations • methodology • analysis • classification
  • 3. Cost of Annotation BRUV •1 hour video = ~2.5-3 hours to analyse •~$143 per drop •$1 219 750 for 8500 drops AUV, TI •5 point analysis •100 images per day for experienced benthic ecologist •Deployment transect (2000-15000 images) Cost inhibiting uptake and expansion of these monitoring methods Source: Euan Harvey (OI-UWA), AIMS
  • 4. Disparate data sources • What is the distribution of kelp or seagrass at a continental scale? • What are the effects of climate change on marine habitats on a continental scale?
  • 5. Centralised, standardised data holdings (web app)
  • 6. CATAMI classification scheme
  • 7. 81 page document
  • 8. Implementation • Within image • Whole image (dominant biota/substrata) a) <20% FOV b) 20 – 40% FOV c) 40 – 60% FOV d) 60 – 80% FOV e) >80% FOV • Qualifiers (e.g. bleached, dead, veneer..)
  • 9. Further information • AMI-Classification-Scheme
  • 10. Annotation tool • Assist researchers to save time • Option to use existing tool(s) then map into CATAMI classification •
  • 11. Still image annotation CPCe
  • 12. DEMONSTRATION See to view screencast
  • 13. Approach to project • User focus (Technical Working Group) • Usability testing •
  • 14. Conclusion CATAMI aims to: 1)increase efficiency 2)enable new science CATAMI is user-driven 12 sprints left to shape Next steps - Broadscale classification - Machine learning Image Credit: NASA
  • 15. Find out more • Site - • Sandbox - (latest version) • Blog - http://catami- • Github - • Email –
  • 16. Acknowledgements • Franzis Althaus, CSIRO • Nicole Hill, UTAS • Keith Hayes, CSIRO • Jamie Colquhoun, AIMS, • Christine Schönberg, AIMS • Mark Case, AIMS • Rachel Przeslawski, GA • Renae Hovey, UWA • Alan Jordan, Dept. of Primary Industries NSW • Ben Radford, AIMS • Gary Kendrick, UWA • Euan Harvey, UWA • Dan Marrable, iVEC • Derrick Wong, iVEC • Stefan Williams, ACFR • Renata Ferrari, USyd • Tony Rees, CSIRO • Graham Edgar, Utas • Daniel Steinberg, ACFR • Ariell Freidman, ACFR • Lachlan Toohey, ACFR • Mark Gray, iVEC • Fiona Scott, UTas • Luke Edwards, iVEC • Dr Jenni Harrison, iVEC, • Dr Ziggy Marzinelli, Usyd • Jane Fromont (WA Museum), • Ty Hibberd (AAD), plus many more…
  • 17. The many collaborators on the project and the Technical Working Group (TWG) who without their support this project would not be possible This project includes development funded by the Australian National Data Service (ANDS, and the National eResearch Collaboration Tools and Resources (NeCTAR, ANDS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative. NeCTAR is an Australian Government project conducted as part of the Super Science initiative and financed by the Education Investment Fund. The University of Melbourne has been appointed the lead agent by the Commonwealth of Australia, Department of Industry, Innovation, Science, Research and Tertiary Education. Acknowledgement