Machine learning to analyse climate with satellite data

From 1 February to 29 February, Central Saint Martins’ Digital Innovation Season will explore and celebrate creativity in the digital space. This platform offers public talks and workshops for CSM students.The 2024 edition will focus on ‘Planetary Sensing’. This takes a ‘planetary’ perspective related to the climate crisis, social impact, and learning, teaching and research that engage cutting edge technologies like AI, IoT, wearables, and spatial computing. Rather than imagining a schism between the local and the global, a planetary perspective considers the material conditions of the planet - and how the earth understands and communicates through arrays of satellites, sensors, swarms, and servers that influence geographies, climate, and societies.

Central Saint Martins: Digital Innovation Season

<h3><strong>This event is for CSM Students only - please note, applications are now closed.</strong></h3><h3>In this workshop, participants will learn to use machine learning to categorise images, from a dataset of satellite images, with the goal to identify extreme climate events. They will first be presented with recent research on the concepts of ‘remote sensing’, data mining and machine learning models. They will then have hands-on experience of pulling satellite data, analysing multi-spectral images, and differentiating climate-related imagery.</h3><h3>Participants will learn to access, visualise, and work with satellite data from any location on Earth between two selected dates. They will learn to download multi-spectral Remote Sensing data from the open-access archive of the European Space Agency (ESA) Sentinel-2 mission. </h3><h3>Through this, they will learn several basic analytical tools of working with these datasets, methods for calculating various useful summative indices (e.g. NDVI) and baseline methods to conduct change detection on a series of images over a targeted area.</h3><h3>Finally, the workshop will also touch upon a novel deep learning model called RaVAEn to detect disaster events (such as floods, fires, hurricanes). </h3>

<p>Wednesday 20 April 2022 2-5pm BST
</p><p>Online
</p><p>Max 15 students
</p><p>No prerequisites, Google account required with 5GB free on drive 
</p><p>Software: Google Earth Engine
</p>

<p><strong></strong><a href="https://previtus.github.io/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer"><strong>Vít Růžička </strong></a>is computer scientist, PhD candidate at the University of Oxford. He has worked as a lecturer at the University of the Arts London in the Creative Computing Institute. Previously, he has worked at EcoVision Lab at ETH Zurich, a research lab focussed on machine learning, computer vision and remote sensing for environmental science and geosciences. Vit has also done research on machine learning at Carnegie Mellon University.</p>

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Machine learning to analyse climate with satellite data

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Machine learning to analyse climate with satellite data

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