Principal component analysis (PCA) visualisation of the embeddings of the Major TOM Core-S2L2A dataset computed using the MMEarth model. Different colours indicate the position of each location along the 3 most prominent change dimensions in the dataset perceived by the model. AI models applied to large-scale data provide global and efficient methods of viewing planetary data that is not possible with traditional methods or manual inspection.
Artificial Intelligence (AI) is a rapidly evolving field that has many potential implications for Earth Observation (EO) sector in Europe and globally. Fundamentally the implementation of AI in EO data production, dissemination and exploitation pipelines will result in the ability of users to browse and interact with EO data much faster, much more efficiently and to query and process data with much less compute.
AI for Copernicus: Vision and Development Perspectives, funded by the European Space Agency, will provide a summary of the current progress in AI, its applications to EO, and how the upcoming advancements in technology will translate into operational requirements for the programme.
This analysis is closely tied with the expected user needs and stakeholder feedback. Asterisk Labs is in the process of gathering input from the wider community and if you would like to contribute to this, please contact us at hello@asterisk.coop.
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