My New Year resolution is to read more papers. To be consistent, I decided to post here (at least) one paper per week. I will look for interesting (and, possibly, not so widespread) AI4EO papers. My target is 50 papers! I am open to suggestions on the report and on papers!

#4 Galileo Galileo is a family of pretrained RS models designed to flexibly process multimodal RS data. It has two loss: one in the pixel space, one in the latent space. ⬆️: multi-modal/temporal/sensor ⬇️: why just using Sentinel data?

#3 GlaViTU This paper presents a novel world-wide dataset and a convolutional-transformer, Glacier-VisionTransformer-U-Net (GlaViTU), for multitemporal and global glacier mapping. ⬆️ relevant task and nice results ⬇️ weak zero-shot transferability?

@dipak_sharda hi, not my work, you can try and contact the authors :)

#1 Diffusion Models for RS This paper provides a comprehensive review of the applications of diffusion models in remote sensing ⬆️ excellent entry point ⬇️ not sure about the statement about the "inherent denoising ability" of diffusion models

Hellooooo 🚀 I'll make it swift: I have just started my new position as Internal Research Fellow at European Space Agency - ESA Phi-Lab I am very happy because it looks like a great place where to do research and because I am back in my beloved hometown, Rome 😍

I start the new challenge this week :) Also, other very cool personal news is coming out So stay tuned if interested ✨

#41 Beyond Grid Data GNNs open new possibilities for EO, handling irregular, multi-source datasets for smarter weather forecasts, disaster relief, etc.. ⬆️: excels at non-Euclidean spatial data ⬇️: limited scalability across diverse data (?)

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 GFMs ✨🌏

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:

🚀🌏 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!

RT @robmarkcole: TorchSpatial offers a comprehensive framework and benchmark suite designed to advance Spatial Representation Learning (SRL…