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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

#71

Post by RosanaAnaDana »

bookofjoe wrote: post #46
philosophygeek wrote: post #40

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…

>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. https://spectrum.ieee.org/commercial-satellite-imagery

I regularly use up to 3cm aerial imagery.

There are many very nice commercial products available.

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

#72

Post by gulyams »

justinwp wrote: post #55
mrbgty wrote: post #42

Earlier quoted context omitted.

what happened with your company?

he ran into the ground without a vision and excess spending on bar tabs and the startup life

If I'm correct and philosophygeek is Mark Johnson, he cofounded Descartes Labs. It was a pretty cool company with some quite impressive technology. He (they) did a lot.

I'm not far from bashing the VC scene and the adjacent startup culture, but your overly cynical comment was too much even for me. More intellectual humility and less cheap soundbites would benefit society a lot.

If you're interested, he wrote about it: https://philosophygeek.medium.com/meditations-a-requiem-for-...

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

#73

Post by RosanaAnaDana »

blincoln wrote: post #45
philosophygeek wrote: post #40

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…

> 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…

Like... no?

InfSar (InSar, SAR, whatever we're calling it these days) isn't a drop in replacement for anything. Its really neither here nor there when it comes to the utility of other dataset. Infsar is amazing, dgmw, but its stands on its own and has its own advantages/ disadvantages.

The ocs point stands. Satellite data is tough because there is a shit ton of atmosphere between you and the target. That issue doesn't go away with infsar and especially not if it isnt coincidentally collected with higher resolution spectral data. I've been in the industry for around 15 years. Things have gotten better, but really, its important to understand the context and limitations of specific platforms. Afaik, there is no panacea.

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

#74

Post by RosanaAnaDana »

gulyams wrote: post #72
justinwp wrote: post #55

Earlier quoted context omitted.

he ran into the ground without a vision and excess spending on bar tabs and the startup life

If I'm correct and philosophygeek is Mark Johnson, he cofounded Descartes Labs. It was a pretty cool company with some quite impressive technology. He (they) did a lot. I'm not far from bashing the VC scene and the adjacent startup culture, but your overly cynical comment was too much even for me. More intellectual humility and less cheap soundbites would benefit society a lot. If you're interested, he wrote about it…

Descarte labs folded?

Oh man. I had no idea! These guys were some of my prime competition for years.

The true cost of venture capitol revealed.

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

#75

Post by RosanaAnaDana »

taf2 wrote: post #47
capableweb wrote: post #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 finetu…

Wondering a use case for a model like these would be navigation without GPS?

Not at 10m brah.

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

#76

Post by RosanaAnaDana »

ritwikgupta wrote: post #61

This simply is not true! NASA and IBM need to do further literature review and rely less on press releases. There are larger foundation models for geospatial imagery available. Our pre-training method, Scale-MAE [0], has 323M parameters, makes encoders robust to changes in satellite imagery resolution, and is therefore trained on satellite imagery of all resolutions. Work out of SI Analytics [1] presents a 2.4B param…

If the models aren't available in a repo for download do they actually exist?

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

#77

Post by RosanaAnaDana »

ifdeluxe01 wrote: post #49

Can 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 wit…

So the big issue is can be summed up as one present across many domains of ML, but particularly challenging in remote sensing. ML has seen rapid advances in spaces where foundational models capture the variance of a domain the latent space of a model. By training on very large dataset, the variance becomes encoded in that latent space; lots of training data implies that the latent space contains meaningful variation. This is what powers things like stable diffusion. A model trained on a very large dataset was connected with another dataset, in latent space. The image model wasn't starting from square 0. It had meaningful variation encoded into it.

Enter remotely sensed dataset. They are monumental in scale. So much so that the 804 chips they submitted here is pretty much laughable. Likewise, some parameters of the data have inherent meaning; the pixel dimensions correspond to real world measurements; the bands are specific spectral windows.

Its similar but far more than just working with RGB cell phone camera data.

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

#78

Post by RosanaAnaDana »

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 .

Its the largest *published* geospatial model. There are plenty of models being developed daily with more than 100m parameters.

I think its just that there is a dearth of publishing on the advances in geospatial ml.

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

#79

Post by ritwikgupta »

jafo1989 wrote: post #69
ritwikgupta wrote: post #61

This simply is not true! NASA and IBM need to do further literature review and rely less on press releases. There are larger foundation models for geospatial imagery available. Our pre-training method, Scale-MAE [0], has 323M parameters, makes encoders robust to changes in satellite imagery resolution, and is therefore trained on satellite imagery of all resolutions. Work out of SI Analytics [1] presents a 2.4B param…

I'm interested. Are your models open sourced like the IBM model, and are they easily accessible on HuggingFace?

They are open sourced (code and weights) [0], but not accessible on HuggingFace.

[0] https://ai-climate.berkeley.edu/scale-mae-website/

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

#80

Post by jafo1989 »

ritwikgupta wrote: post #79
jafo1989 wrote: post #69

Earlier quoted context omitted.

I'm interested. Are your models open sourced like the IBM model, and are they easily accessible on HuggingFace?

They are open sourced (code and weights) [0], but not accessible on HuggingFace. [0] https://ai-climate.berkeley.edu/scale-mae-website/

You might want to think about adding them to HuggingFace, and making them HuggingFace API compliant to lower the barrier to entry.

The IBM offering is appealing not because it is "first" or the "best", but because it is accessible. A lot of institutions / enterprises (including my own) are able to leverage transformer models because HF has done so much to lower the barrier to entry with their hub, documentation (model cards) and API.

How else would I have found your models but for an IBM press release?

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