Large Language models in Earth Observation

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. 

CARITRO Project: Snow droughts e green water: how climate change modifies the hydrological cycle in the Alpine Region.

Due to the impact of climate change, the Alpine region is experiencing a dual effect: a decrease in snowfall leading to snow droughts, and an increase in water losses through evapotranspiration, also known as green water. These changes have significant implications for the sustainable management of water resources and the preservation of ecosystems. This project, funded by the CARITRO foundation, aims to address these challenges by developing innovative models to accurately quantify snow melt and evapotranspiration losses. The ultimate goal is to provide practitioners with user-friendly calculation tools that are more advanced than traditional lumped models but less complex than intricate "process-based" 3D models. Initially proposed by Niccolò Tubini, the project has been taken up by John Mohd Wani with minimal modifications.  

The complete project plan can be found here