uoooo great news, how many are you trying to cover?

I start the new challenge this week :)

Also, other very cool personal news is coming out

So stay tuned if interested ✨

Did we overlook something? Are you interested in this kind of topic?

We are already considering future updates, so feel free to reach out to give feedbacks and to talk about geospatial foundation models

✨🌏

A great team collaborated on it!

Thx @yurujia.bsky.social @lebellig.bsky.social @nshaud.bsky.social and all the others 🤩

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We observed interesting insights, such as:

1. generally speaking GFMs don't really excel when compared to supervised baselines

2. for some specific scenarios (e.g. HR data), it makes sense to use them

3. multi-temporal data are still under-estimated

other insights in the paper!

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With this benchmark (PANGAEA), we tried to address the following research challenges:

* provide a robust evaluation protocol to benchmark GFMs
* investigate GFMs capabilities, with a focus on a) domain generalization, b) comparison to supervised baselines, c) performance with limited labels

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We collected 11 datasets to create an inclusive, diverse benchmarks, based on these criteria:
* application domain
* geographical distribution
* type of task
* modality
* temporality

Spoiler: no patch-level classification tasks are included!

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Another paper shows that global models are not always the best choice.

If you are interested in this topic, and in geospatial foundation models in general, next week we will publish an interesting pre-print, connected to our Pangaea repo

Check it here: github.com/yurujaja/pan...

also, in the past I posted about this interesting benchmark paper:

#32 GeoFMs for crop type mapping

it investigates the ability of geoFMs to transfer to new geographic regions in agriculture

⬆️the pivotal topic for real-world applications
⬇️the limited number of geoFMs

arxiv.org/pdf/2409.09451

here is the GitHub of PANGAEA code (that we used for the experiments):
github.com/yurujaja/pan...

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If you want to see how geospatial foundation models are working in real-world tasks w.r.t. supervised baselines, stay tuned cause next week we are releasing the pre-print of PANGAEA, showing interesting results on this topic!