this is just PR newswire hype, calling it foundation model is a stretch. People have been training nets on satellite imagery for a long time, I don't even see anything here that would need a foundation model, most of them are vanilla CNNs that work relatively well, the problem has mainly been the data pipeline of different gsds and nadir of images which makes layering difficult.
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
#42I 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…
what happened with your company?
Re: IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face
#43Keep 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.
We want much larger models, not because they're "cool," but because they exhibit capabilities that tiny models don't exhibit, including the ability to perform new tasks for which they were not trained, without requiring finetuning.
Re: IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face
#44Earlier quoted context omitted.
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.
We want much larger models, not because they're "cool," but because they exhibit capabilities that tiny models don't exhibit, including the ability to perform new tasks for which they were not trained, without requiring finetuning.
I question the assumption that fine-tuning should always be avoided.
If a model is going to be used many times for a specific use case, it is far cheaper and uses far less energy to fine tune a small model once and run it on cheap low-power hardware than it is to continuously run a huge, do-everything model on expensive, high-power hardware. Enormous models are great for exploration and for general purpose applications like ChatGPT, but I think that we will find over the next few years that smaller, purpose-built models will continue to dominate in applications like geospatial analysis.
Re: IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face
#45I 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…
> 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.
Are the coarseness and cloud aspects going to become less of a factor now that there are commercial high-resolution synthetic aperture radar imagery providers? I'm just a hobbyist, but the imagery I've seen is sharp, and it even caught the NRO's attention.[1]
[1] https://spacenews.com/national-reconnaissance-office-signs-a...
Re: IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face
#46I 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…
>Satellite data is extremely idiosyncratic. It's coarse (~10m at best)
>The best commercially available spatial resolution for optical imagery is 25 cm, which means that one pixel represents a 25-by-25-cm area on the ground—roughly the size of your laptop.
Re: IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face
#47Here 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 finetu…
Wondering a use case for a model like these would be navigation without GPS?
Re: IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face
#48I 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…
I think it is very important for people to understand that terrestrial sensors are orders of magnitude cheaper for most applications, and are typically far more accurate too. There's a reason why most remote sensing companies go out of business fairly quickly.
Re: IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face
#49Can someone enlighten me as to how a foundation model is better suited for visual geospatial data analysis (satellite images) versus an intelligently designed "filter?"
For example: you can easily use Photoshop to distinguish between various hues, apply a mask based on the selection, and then apply a color overlay, essentially "highlighting" a particular hue with another. (I imagine a similar approach can be used with hyperspectral imaging as well.)
As visual data is color-based, why can't such "dumb" filters be used? Why is this an AI challenge, let alone one well suited to the flexibility of foundation models?
*Edit: I believe I may have answered my own question. I assume the advantage this approach has is in the efficient bulk analysis of geospatial data. Platform consumes visual data, and spits out numerical data based on an analysis of the images. That numerical data can then be manipulated and fed into prediction models.
Re: IBM and NASA Open Source Largest Geospatial AI Foundation Model on Hugging Face
#50I 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?
Is this any more useful: https://research.ibm.com/blog/nasa-hugging-face-ibm