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: herehttp://youtu.be/gW0MUOQH80M Actual web application - http://sandbox.catami.org/

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

10. 81 page document

12. 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..)

13. Further information
•https://github.com/catami/catami/wiki/CAT
AMI-Classification-Scheme

14. Annotation tool
• Assist researchers to save time
• Option to use existing tool(s) then map into
CATAMI classification
• http://sandbox.catami.org/

15. Still image annotation
CPCe

16. DEMONSTRATION
See http://youtu.be/gW0MUOQH80M to view screencast

17. Approach to project
• User focus
(Technical Working Group)
• Usability testing
• https://github.com/catami/catami/wiki

18. 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

19. Find out more
• Site - http://catami.org/
• Sandbox - http://sandbox.catami.org/
(latest version)
• Blog - http://catami-
australia.blogspot.com.au/
• Github -
https://github.com/catami/catami
• Email – catami@ivec.org

20. 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…

21. 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, http://ands.org.au) and the National eResearch Collaboration Tools and Resources
(NeCTAR, http://nectar.org.au).
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