Project proposal details
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Using AI to annotate images from the deep-sea
Project based at
Zoological Society of London
1. Using AI to annotate seabed images from Greenland
Benthic (seabed) habitats are an important part of the marine ecosystem. However, studying these environments can be challenging, particularly in deep-seas and polar environments, which means these are some of the least well understood habtiats on earth. Researchers at IoZ have been performing annual imaging surveys of the seabed in West Greenland since 2011 (https://www.zsl.org/greenland). We have been using seabed videos to describe, document and analyse life on the seabed (https://youtu.be/j84wFEJuseI) and assess the impact of trawling on benthic habitats (https://dx.doi.org/10.1093/icesjms/fsw206).
A fundamental part of this work is examining animals on the seabed seen in benthic imaging surveys. Often this requires looking at and annotating hundreds of images to record species observations. This is time consuming and quality of annotations can be dependent on the person assessing the images (https://doi.org/10.1016/j.pocean.2016.07.005). Artificial Intelligence and machine learning have been used in a few cases to identify and record animals on the seabed (https://doi.org/10.1371/journal.pone.0207498). This project will assess the abililty of the MAIA software to identify widespread fauna on the seabed in West Greenland, and use these data for community analysis to define, map and document the major biotic habitats of the region.
This is an opportunity to gain an understanding of seabed habitats by examining video footage from surveys of the Greenland continental shelf, with training in the image annotation plaform Biigle (https://doi.org/10.3389/fmars.2017.00083 - https://biigle.de), and learn about cutting edge technology to automatically identify fauna from images.
Project proposal limitations
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Available date limitations
Winter (January), Spring (April-May)