University of Cambridge > Talks.cam > CEDSG-AI4ER > Unsupervised Iceberg Detection in Satellite Images Using Recurrent Principal Component Analysis

Unsupervised Iceberg Detection in Satellite Images Using Recurrent Principal Component Analysis

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The shape and distribution of icebergs and their trajectory through the oceans from source, to where they break up and melt, can be diagnostic of ice-sheet dynamics, and ocean and atmospheric conditions. Existing methods to detect icebergs in satellite imagery are optimised for open ocean and work less well for icebergs within the sea ice pack. Different conditions for each satellite image also mean that one method does not fit all situations. This talk explores the possibility of making the algorithm data dependent while touching upon specific difficulties in the development of the algorithm which are intrinsic to other data science problems.I will also ponder the value of simplicity.

This talk is part of the CEDSG-AI4ER series.

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