If you've grasped the concepts discussed in the previous post, you're likely poised to explore how to effectively integrate them into your hydrological endeavors. However, you're not starting from scratch. Institutions like ESA and NASA have already outlined some of their applications through a series of posts and contributions. Machine Learning undoubtedly plays a significant role in Earth Observation and remote sensing analyses (see for instance here for an overview) or browse the activities of the RSLab for a perspective from the University of Trento.
Here they are a list of interesting links that I collected:
- NASA and IBM Openly Release Geospatial AI Foundation Model for NASA Earth Observation Data
- Integrating Earth Observation (EO) with Large Language Models (LLMs): Towards A Multimodal EO-Language Model
- Linking Large Language Models for Space Domain Awareness (The topic can look like quite different from our central one but the connection is the management of large amount of data that should be merged for decision making)
- Large language model agent to interact with Earth Observation Data
- Imagining the Future of Large Language Models and Open Science
After having read them I found that those links are give suggestions but at the end kind of work around the technical questions which I am looking for.
I am sure that there exist many other resources on this topic, so, please do not hesitate to point my attention to them and I'll make the list grow.
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