Artificial Intelligence utilizing Space assets for Science discovery

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Brief description of the context of the research including potential impactThe 2017-2027 Decadal Survey for Earth Science Applications from Space finding 1.1 states that Space-based Earth Observations provide a global perspective of Earth that has transformed our "scientific understanding" of our planet, and the vantage point of space enables us to see the extent to which Earth's ever-changing processes influence our lives. However, the volume of data generated daily by Earth Observation (EO) satellites is far too great for humans to conceivably digest, analyze, and synthesize into meaningful decisions and strategies for climate change mitigation and disaster preparedness. The use of Artificial Intelligence (AI) in combination with space assets can maximize the scientific return of space missions through revealing new connections, aid in autonomous decision making, and improve scientific understanding of complex relationships between ecosystems.Aims and ObjectivesThe aims of this PhD research are to utilize space-based assets for scientific research related to climate disaster mitigation, preparedness and response, and aid in scientific understanding through the use of AI. Deep Learning will be explored in this work, which can unveil previously unknown relationships through extracting information from highly-dimensional data via convolutional neural network architectures to learn spatiotemporal features from timeseries EO datasets. Interpretability and uncertainty quantification of models will also be explored in this work. Interpretability is important as it is crucial to be able to effectively communicate how models generate predictions to stakeholders, policy makers, and governments, as these entities are less likely to adopt these AI solutions as reliable if not clearly understood.Novelty of the research methodologyThe novelty of this research will be the identification and formulation of AI and Machine Learning models that can be applied across scientific domains and use cases. The focus on model explainability, interpretability, and uncertainty is at the beginning stages of exploration for researchers within the AI for EO field, which will be critical to implementing these technologies for real-world use.Alignment to EPSRC's strategies and research areasThis research aligns with multiple EPSRC research areas, namely Artificial intelligencetechnologies, operational research, and the UK climate resilience program.Any companies or collaborators involved The PhD work will be supervised by Professor Yarin Gal and Senior Research Fellow Freddie Kalaitzis. Collaborations with the Satellite Applications Catapult and Deimos Space through an industrial studentship will provide EO expertise to support the research