Machine Learning for Earth Observation 2024

Recent years have witnessed a dramatic increase in the acquisition of remote sensing observations from satellite, aircraft and drone-based sensors, and in-situ devices. Technological advances have led to improvements in measurement resolution and precision, which is shifting the paradigm of Earth observation from data scarcity to data abundance. While these data have enormous potential for helping us achieve a range of global challenges, such as meeting the United Nations Sustainable Development Goals, identifying the optimal approaches for handling and analysing these ever-growing datasets remains a challenge. Recent breakthroughs in AI/ML offer promising solutions to these challenges, including automated identification and extraction of key observations, predicting future trends, identifying key environmental factors, and dealing with noisy signals under uncertainty.   
 
This is the second ML4EO workshop following a successful pilot event in 2023. The broad aim is to bring together remote sensing researchers, data science and AI experts, and industrial/third sector partners to build links and share ideas. We hope the workshop will promote further collaboration and help develop research proposals to address specific global challenges. 
 
There are several different ways to participate in this event: 
• Attend the workshop as a delegate
• Oral presentation (e.g. 10 mins + 5 mins discussion) 
• Poster presentation (ideal for preliminary results or ‘works in progress’)
• Chairing/contributing to a thematic discussion session.

Please use this link to register and purchase a ticket. Further information can be found on the event webpage using this link. A detailed schedule will be circulated closer to the event.

Please use the Registration link to register for the event and purchase a ticket. If you would like to give an oral presentation or submit a poster, please submit a title/abstract using the Abstract Submission link.
 
This event is supported by the University of Exeter via the Global Systems Institute (GSI), the Institute for Data Science and Artificial Intelligence (IDSAI) and the Environmental Intelligence Research Network.

Further details can be found on the dedicated ML4E0 2024 website.

Any queries?

Please contact the ML4EO Organising Commitee, a.laskey@exeter.ac.uk.

We hope to see you in Exeter!

ML4EO Committee