AI Adventures in Azure: Ice Surface Classifiers

For this post I will introduce what I am actually trying to achieve with the AI for Earth grant and how it will help us to understand glacier and ice sheet dynamics in a warming world.

The Earth is heating up – that’s a problem for the parts of it made of ice. Over a billion people rely directly upon glacier fed water for drinking, washing, farming or hydropower. The sea level rise resulting from the melting of glaciers and ice sheets is one of the primary species level existential risks we face as humans in the 21st century, threatening lives, homes, infrastructures, economies, jobs, cultures and traditions. It has bee projected that $14 trillion could be wiped off the global economy annually by 2100 due to sea level rise. The major contributing factors are thermal expansion of the oceans and melting of glaciers and ice sheets, which in turn is primarily controlled by the ice albedo, or reflectivity. However, our understanding of albedo for glaciers and ice sheets is still fairly basic. Our models make drastic assumptions about how the albedo of glaciers behaves, some assign a constant value to it, some assume it varies as a simple function of exposure time in the summer, and the more sophisticated models use radiative transfer but on the assumption that the ice behaves in the same way as snow (i.e. it can be adequately represented as a collection of tiny spheres). Our remote sensing products also struggle to resolve the complexity of the ice surface and fail to detect the albedo reducing processes operating there, for example the accumulation of particles and growth of algae on the ice surface, and the changing structure of the ice itself. This limits our ability to observe the ice surface changing over time and to attribute melting to specific processes that would enable us to make better predictions of melting – and therefore sea level rise – into the future.

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Aerial view of a field camp on the Greenland Ice Sheet in July 2016. The incredible complexity of this environment is clear – there are areas of bright ice, standing water, melt streams, biological aggregates known as cryoconites and areas of intense contamination with biological growth, mineral dust and soots – none of which is resolved by our current models or remote sensing but all of which affect the rate of glacier melting.

I hope to contribute to tackling this problem with AI for Earth. My idea is to use a form of machine learning known as supervised classification to map ice surfaces from drone images and then at the scale of entire glaciers and ice sheets using multispectral data from the European Space Agency’s Sentinel-2 satellite. The training data will come from spectral measurements made on the ice surface that match the wavelengths of the UAV and Sentinel sensors. I’ll be writing the necessary code in Python and processing the imagery in the cloud using Microsoft Azure, with the aim of gaining new insights into glacier and ice sheet melting and developing an accessible API to host on the AI for Earth API hub. I have been working on this problem for a while and the code (in active development) is being regularly updated on my Github repository. A publication is currently under review.

I have already posted about my Azure setup and some ways to start programming in Python on Azure virtual machines, and from here on in the posts will be more about coding specifically for this project.

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