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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

#31

Post by Hizonner »

fidotron wrote: post #7
romusha wrote: post #5

Earlier quoted context omitted.

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.

So it's some coils of wire wound around a common high permeability core?

Or some kind of weird robot-car hybrid?

I guess since it's a "Vision" transformer, it's probably actually some kind of hallucinogenic plant.

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

#32

Post by naillo »

cs702 wrote: post #19

Keep in mind, even though this is the largest geospatial model, it's still a tiny model, with only 100M parameters. I'd be excited to see what a more substantial, state-of-the-art model could do with geospatial data. Say, a model based on something like ViT-22B, with 22 billion parameters: https://arxiv.org/abs/2302.05442 .

"What about a bigger model" is basically feature creep for ML.

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

#33

Post by [deleted] »

Kiro wrote: post #24
oefrha wrote: post #9

I suggest changing the link to https://huggingface.co/ibm-nasa-geospatial . The currently linked press release is an insufferable corporate PR word salad.

As someone who is not a Hugging Face user your link is much less clear than the submitted link. Really esoteric UI. What am I even looking at? What are Spaces?

[deleted]

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

#34

Post by lolinder »

oefrha wrote: post #9

I suggest changing the link to https://huggingface.co/ibm-nasa-geospatial . The currently linked press release is an insufferable corporate PR word salad.

In addition to what Kiro said (Hugging Face's organization UI is hard to parse), the text on this Organizational Card is written in exactly the same word-salad style, just with even less information.

I prefer the full press release, especially since it already has the link to Hugging Face for those who want it.

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

#35

Post by oefrha »

Kiro wrote: post #24
oefrha wrote: post #9

I suggest changing the link to https://huggingface.co/ibm-nasa-geospatial . The currently linked press release is an insufferable corporate PR word salad.

As someone who is not a Hugging Face user your link is much less clear than the submitted link. Really esoteric UI. What am I even looking at? What are Spaces?

I’m not familiar with HuggingFace at all (not in the AI space) but I clicked on the demos and models on that page and was able to learn what this is all about. On the other hand, I read the press release in full and left with no idea what the model is supposed to be.

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

#37

Post by lolinder »

cs702 wrote: post #19

Keep in mind, even though this is the largest geospatial model, it's still a tiny model, with only 100M parameters. I'd be excited to see what a more substantial, state-of-the-art model could do with geospatial data. Say, a model based on something like ViT-22B, with 22 billion parameters: https://arxiv.org/abs/2302.05442 .

Personally, I'm excited to see ML researchers doing cool stuff with small models again!

With LLMs taking over the spotlight it's easy for people to forget that not everything needs billions or trillions of parameters. Stable Diffusion fits comfortably in my 8GB of VRAM and can generate amazing images. I'd love to see more research like this in smaller models that can be used on cheap consumer hardware.

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

#38

Post by garyfirestorm »

The demo misidentifies West Bengal, India flood to a location in Pakistan. Either the title is incorrect or the model is simply wrong. The demo should have been proof read before publishing :/

https://youtu.be/9bU9eJxFwWc?t=28

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

#39

Post by qwertox »

The core information:

> The model – trained jointly by IBM and NASA on Harmonized Landsat Sentinel-2 satellite data (HLS) over one year across the continental United States and fine-tuned on labeled data for flood and burn scar mapping — has demonstrated to date a 15 percent improvement over state-of-the-art techniques using half as much labeled data. 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

#40

Post by philosophygeek »

I wish the press release had a bit more detail about what this model actually does and whether it's actually useful for the suggested use cases.

However, make no mistake: this is for the scientific community and will not help geospatial data to be commercialized. No one cares about your geospatial crop model or that you can identify energy infrastructure or that there's some activity around that copper mine. Well, at least no one cares that will actually pay you.

(FWIW, I cofounded a geospatial analytics company)

Satellite data is extremely idiosyncratic. It's coarse (~10m at best), infrequent (every few days at best), and oh you have to deal with the fact that the planet is covered in 50% clouds at any moment. Satellite data works best on things that don't move, that are fairly large, and change infrequently. If you find a use case that satisfies those conditions and want to make money, then you need to find a problem that terrestrial sensors haven't solved. And if you find that problem, the cost of building, training, and running your model (plus the cost of the data!) has to be less than the marginal value of your model. Good luck finding those use cases.

The US Government is special. We don't know what's going on in North Korea or Ukraine or the South China Sea so we buy high resolution imagery over those areas (30cm) at great cost. Large ag companies and oil companies know what's going on within their own facilities; and price gives them information about the rest of the supply chain.

In other words, this might be an interesting announcement for scientists, but it won't change the geospatial market at all.

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