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IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face

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Re: IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face

#3
“Model” is a dangerously overloaded word in 2023, possibly worse than Object in 1996.

When I worked on ML inference I would tease the researchers with the question “what is a model?” in the hope they would say something that constrained it in any way, but no, a model can be anything at all and is whatever you want it to be.

As such this press release is perfect nonsense, which is kind of appropriate for a Watson offshoot. If there is something interesting here it doesn’t succeed in telling you what it is.

Re: IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face

#4
post #2

What makes this a “foundation model”?

They are using the word to mean that it is not single-purpose or task-specific and there is an ability to fine-tune / adapt it for new use cases without retraining the whole thing.

> With additional fine tuning, the base model can be redeployed for tasks like tracking deforestation, predicting crop yields, or detecting and monitoring greenhouse gasses. IBM and NASA researchers are also working with Clark University to adapt the model for applications such as time-series segmentation and similarity research.

Re: IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face

#5
post #3

“Model” is a dangerously overloaded word in 2023, possibly worse than Object in 1996. When I worked on ML inference I would tease the researchers with the question “what is a model?” in the hope they would say something that constrained it in any way, but no, a model can be anything at all and is whatever you want it to be. As such this press release is perfect nonsense, which is kind of appropriate for a Watson offs…

So what's the better word?

Re: IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face

#7
post #5
post #3

“Model” is a dangerously overloaded word in 2023, possibly worse than Object in 1996. When I worked on ML inference I would tease the researchers with the question “what is a model?” in the hope they would say something that constrained it in any way, but no, a model can be anything at all and is whatever you want it to be. As such this press release is perfect nonsense, which is kind of appropriate for a Watson offs…

So what's the better word?

What it actually is: https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M

“Prithvi is a first-of-its-kind temporal Vision transformer”

That is enormously more specific and interesting, and the word model is absent.

Re: IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face

#8
Here are a bunch of demos of different use cases:

- https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-10... - This demo showcases how the model was finetuned to detect water at a higher resolution than it was trained on (i.e. 10m versus 30m) using Sentinel 2 imagery from on the sen1floods11 dataset

- https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-10... - This demo showcases how the model was finetuned to classify crop and other land use categories using multi temporal data.

- https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-10... - This demo showcases the image reconstracting over three timestamps, with the user providing a set of three HLS images and the model randomly masking out some proportion of the images and then reconstructing them based on the not masked portion of the images

- https://huggingface.co/spaces/ibm-nasa-geospatial/Prithvi-10... - This demo showcases how the model was finetuned to detect burn scars

More/same but different source information:

- From NASA - https://www.earthdata.nasa.gov/news/impact-ibm-hls-foundatio...

- From IBM - https://research.ibm.com/blog/nasa-hugging-face-ibm

- From huggingface - https://huggingface.co/ibm-nasa-geospatial

Re: IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face

#10
post #3

“Model” is a dangerously overloaded word in 2023, possibly worse than Object in 1996. When I worked on ML inference I would tease the researchers with the question “what is a model?” in the hope they would say something that constrained it in any way, but no, a model can be anything at all and is whatever you want it to be. As such this press release is perfect nonsense, which is kind of appropriate for a Watson offs…

If you think of a model as a function approximator with an error that can only be characterized empirically, it's not a bad term at all to describe the class of algorithms ANNs belong to.

It's a real shame that the term is being abused so badly, because it's really appropriate in a lot of these cases. Or rather, it would be if people used it mindfully.

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