Revolutionizing Marine Mammal Detection using Geospatial Artificial Intelligence

 
GAIA
Geospatial Artificial Intelligence for Animals:
Developing an Operational System for
Detecting Marine Mammals in Very High
Resolution Satellite Imagery
 
Christin B. Khan, Kimberly T. Goetz, Hannah C. Cubaynes, Caleb Robinson, Erin
Murnane, Tyler Aldrich, Meredith Sackett, Penny J. Clarke, Michelle A. LaRue,
Timothy White, Kathleen Leonard, Anthony Ortiz and Juan M. Lavista Ferres
 
GLOBAL INTEREST
IN MARINE ANIMALS
 
Government agencies, academic
research, commercial institutions,
general public
 
VISUAL
 
Vessels, Aircraft, UxS
 
TAGGING
 
Satellite-monitored tags
 
ACOUSTIC
 
Buoys, Gliders, Towed Arrays
 
MONITORING PLATFORMS
 
             PROOF OF CONCEPT
 
collage
 
FEW IMAGES
 
Only a single image or a
handful of images
 
WHALE HOTSPOTS
 
Areas of known aggregations
of species of interest
 
IDEAL CONDITIONS
 
Clear skies, sunny day, and low
wind conditions
 
LIMITATIONS
 
SATELLITES
 
Resolution is detailed enough
for species identification
 
AI
 
Incredible advances in
machine learning
 
CLOUD
 
Potential for rapid image
processing without downloading
 
THE TIME HAS COME
 
IMAGINE THE FUTURE!
GAIA
G
eospatial 
A
rtificial 
I
ntelligence for 
A
nimals
 
TASKING OVER WHALES
 
 
MANUAL
 
Dr. Hannah Cubaynes
ArcMap and ArcPro Protocols
 
CROWDSOURCED
 
MAXAR’S GeoHIVE
Crowdsourcing Platform
 
STREAMLINED
 
Microsoft AI for Good
‘Human in the Loop’ tool
 
ANNOTATION
 
INTERESTING POINTS
 
Window statistics
Rolling statistics​
 
Generative modeling
 
         TRADE OFFS
 
59 whales / 50,325 interesting points
58 whales / 26,392
56 whales / 4,557
 
whale
 
MANUAL
 
3.5 hrs/100 sq km
 
 
 
 
(Cubaynes et al 2019)
 
STREAMLINED
 
10 sec/image
 
 
 
 
Microsoft AI for Good
‘Human in the Loop’ tool
 
   PROCESSING TIME
 
ACTIVE LEARNING
 
IMAGE ACCESS
 
Cost, Priority, Spatial and
Geographic limitations,
tasking & downloading
 
LICENSING
 
Inability to share data makes
it challenging to build up
annotation dataset
 
WEATHER
 
High sea state and clouds may
impact the utility of this
platform
 
CHALLENGES
 
OPERATIONAL SYSTEM
 
end
 
journal
Slide Note

Hi :-) My name is Christin Khan and I’m a marine biologist at the NOAA Northeast Fisheries Science Center in Woods Hole.

I’m thrilled to be here and have this opportunity to share with you the new GAIA initiative to detect marine animals from very high resolution satellite imagery.

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Delve into the groundbreaking development of an operational system utilizing geospatial artificial intelligence to detect marine mammals in very high-resolution satellite imagery. Explore the global interest, monitoring platforms, proof of concept, limitations, future possibilities, and innovative annotation methods discussed in the study.


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  1. GAIA Geospatial Artificial Intelligence for Animals: Developing an Operational System for Detecting Marine Mammals in Very High Resolution Satellite Imagery Christin B. Khan, Kimberly T. Goetz, Hannah C. Cubaynes, Caleb Robinson, Erin Murnane, Tyler Aldrich, Meredith Sackett, Penny J. Clarke, Michelle A. LaRue, Timothy White, Kathleen Leonard, Anthony Ortiz and Juan M. Lavista Ferres

  2. GLOBAL INTEREST IN MARINE ANIMALS Government agencies, academic research, commercial institutions, general public

  3. MONITORING PLATFORMS VISUAL ACOUSTIC TAGGING Vessels, Aircraft, UxS Buoys, Gliders, Towed Arrays Satellite-monitored tags

  4. PROOF OF CONCEPT

  5. LIMITATIONS FEW IMAGES IDEAL CONDITIONS WHALE HOTSPOTS Only a single image or a handful of images Clear skies, sunny day, and low wind conditions Areas of known aggregations of species of interest

  6. THE TIME HAS COME SATELLITES CLOUD AI Resolution is detailed enough for species identification Potential for rapid image processing without downloading Incredible advances in machine learning

  7. IMAGINE THE FUTURE!

  8. GAIA Geospatial Artificial Intelligence for Animals

  9. TASKING OVER WHALES

  10. ANNOTATION MANUAL STREAMLINED CROWDSOURCED Dr. Hannah Cubaynes ArcMap and ArcPro Protocols Microsoft AI for Good Human in the Loop tool MAXAR S GeoHIVE Crowdsourcing Platform

  11. INTERESTING POINTS Window statistics Rolling statistics Generative modeling

  12. TRADE OFFS 59 whales / 50,325 interesting points 58 whales / 26,392 56 whales / 4,557

  13. PROCESSING TIME MANUAL STREAMLINED 3.5 hrs/100 sq km 10 sec/image (Cubaynes et al 2019) Microsoft AI for Good Human in the Loop tool

  14. ACTIVE LEARNING

  15. CHALLENGES IMAGE ACCESS WEATHER LICENSING Cost, Priority, Spatial and Geographic limitations, tasking & downloading High sea state and clouds may impact the utility of this platform Inability to share data makes it challenging to build up annotation dataset

  16. OPERATIONAL SYSTEM

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