Study and summarize the key concepts of the DeepSensor paper

[DaniJonesOcean]

Issue Description:

To build a solid foundation for our Great Lakes temperature sensor placement project, a fairly thorough understanding of the paper 'Environmental Sensor Placement with Convolutional Gaussian Neural Processes' by Tom Andersson et al. is essential. This research underpins much of our project's methodology:

Tom Andersson et al. Environmental Sensor Placement with Convolutional Gaussian Neural Processes. Environmental Data Science (2023)

Key objectives:

• Read the full paper to grasp the primary objectives, methods, and conclusions.
• Focus on understanding the application of DeepSensor and its role in environmental sensor placement.
• You are absolutely not required to work through all the equations. Instead, aim to attain a solid conceptual understanding of the processes and algorithms presented.

Suggested study approaches (just suggestions!):

• Write a concise 2-3 page summary that captures the essence of how DeepSensor functions.
• Consider creating a slide deck that outlines the main points and could be used for presenting the concepts to others.
• Record a brief explanatory video to articulate your understanding of the paper.

Your ability to convey the core principles and functionality of DeepSensor to other researchers will be really useful. Feel free to use diagrams, analogies, or any other tools that facilitate your comprehension and communication of the material.

Once you've completed this task, you should be prepared to:

• Explain the significance of DeepSensor to the project's objectives.
• Discuss the paper's insights and how they inform our strategy.

Additional resources:

As a companion to the above reading, watch these informative videos to reinforce your understanding:

• Tackling diverse environmental prediction tasks with neural processes - Offers a direct explanation from the lead author (1 hour)
• Accompanying Slides for the Video - Some of these may serve as a visual aid for your summary or presentation
• Environmental Sensor Placement with ConvGNPs - a shorter presentation that covers some of the same ideas (15 min)
• Southern Ocean Observing System Mini-workshop on environmental sensor placement with ConvGNPs - a short talk delivered to an observing system design group (20 min)

Review the comprehensive documentation available to solidify your knowledge. This will also be useful as you get used to the software package:

DeepSensor Documentation

Finally, review this possibly useful poster, as it employs DeepSensor for optimal sensor placement in a different context (black carbon):

Paolo Pelucchi et al., Optimal Sensor Placement for Black Carbon AOD with Convolutional Neural Processes, iMIRACLI Summer School / FORCeS annual meeting (2023)

If there are any concepts or sections that are unclear, don't hesitate to reach out for a discussion or further clarification. Enjoy!

[eredding02]

@DaniJonesOcean

DeepSensor is novel in that in its ConvCNP method it is able to use gridded and non gridded data in predictions, being able to spatially interpolate information onto an internal grid and also handle Nan data. Another important feature is that it is able to capture prediction uncertainty, which the project will utilize to select optimal sample sites.

In its applications it combines modeling and data capture in multiple connections. Combining observations with models such as reanalysis can give improved predictions, but it can also drive data collection. The down-the-line outcome of the project will hopefully be the placement of sensors, which can be used to create better predictions and the cycle continues.

[DaniJonesOcean]

@eredding02 Yes! Very nice summary. I like your description of the model-observational improvement cycle.

Feel free to close this comment and put it in the "done" column!