#8 Copernicus-FM This paper introduces: a) a new pre-training dataset; b) a new benchmark dataset; c) a GFM, all based on a diverse set of Copernicus data. ⬆️: really appreciate the grid embeddings part ⬇️: some doubts about claims about generalizability arxiv.org/pdf/2503.11849

🔥 🎯 Parameter-Efficient Adaptation of Geospatial Foundation Models through Embedding Deflection (#7) New preprint around :) Incorporating inductive biases specific to MSI can enhance the fine-tuning of large Earth observation models, pre-trained on RGB arxiv.org/pdf/2503.09493

#6 Lossy Neural Compression for Geospatial Analytics The authors introduce NC and discuss the characteristics of EO and climate data, w.r.t natural images ⬆️: great entry point ⬇️: no baseline exps arxiv.org/pdf/2503.01505

#5 Is SSL on Satellite Imagery Better than ImageNet? A Systematic Study with Sentinel-2 This study pretrains two SSL methods on ImageNet and GeoNet. The improvement with GeoNet is minimal. ⬆️ useful to reduce computation? ⬇️ more considerations about the resolutions? arxiv.org/pdf/2502.10669

#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? arxiv.org/pdf/2502.09356

#3 GlaViTU This paper presents a novel world-wide dataset and a novel convolutional-transformer, named Glacier-VisionTransformer-U-Net (GlaViTU), for multitemporal and global glacier mapping. ⬆️ relevant task and nice results ⬇️ weak zero-shot transferability? www.nature.com/articles/s41...

Globally scalable glacier mapping by deep learning matches expert delineation accuracy - Nature Communications

A deep learning model using open satellite data for scalable, global glacier mapping is developed. This model matches expert-level accuracy, facilitating more reliable glacier monitoring to support cl...

nature.com

#2 Can Location Embeddings Enhance Super-Resolution of Satellite Imagery? It looks like they can :) ⬆️: validating it on a real-world task ⬇️: is it super-resolution or mapping S2 to NAIP? arxiv.org/pdf/2501.15847

#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 arxiv.org/abs/2404.08926

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 😍

Back on social, after a break (can you guess where?) Last year I decided to do a #50paperschallenge I ended up with 43. Still: 🥵 I read more than 50 papers. I just didn't post all 😇 the strategy worked independently of the posted ones For this reason, this year I will do a #40paperschallenge!

#41 Beyond Grid Data GNNs open new possibilities for EO, handling irregular, multi-source datasets (e.g. point clouds) for smarter weather forecasts, disaster relief, etc.. ⬆️: excels at non-Euclidean spatial data ⬇️: limited scalability across diverse data (?) arxiv.org/abs/2411.03223

🚀🚀🌏 Are geospatial foundation models really impactful? Check it in our new pre-print! Welcome to **PANGAEA: a global and inclusive benchmark for GFMs** arxiv.org/abs/2412.04204 Check also the public GitHub repo (other news/updates soon): github.com/VMarsocci/pa... a short thread 🧵

#39 TCH in African savannas Can global SatML models solve local challenges? This study finds local models outperform global & fine-tuned models for TCH mapping in Africa ⬆️: interesting set of research questions ⬇️: what about "generalist" geospatial foundation models? arxiv.org/pdf/2411.14354

#38 SPECIALIZED FOUNDATION MODELS STRUGGLE TO BEAT SUPERVISED BASELINES Specialized FMs in genomics, satellite imaging, and time series, struggle w.r.t. supervised learning pipelines ⬆️: very relevant work ⬇️: just classification, limiting the real-world capabilities* arxiv.org/abs/2411.02796

As I haven't found it out there yet, I made the Women in computer vision started pack. Many more missing, please let me know how is already in bsky to add them! go.bsky.app/BowzivT

The strain on scientific publishing: we set out to characterise the remarkable growth of the scientific literature in the last few years, in spite of declining growth in total scientists. What is going on? direct.mit.edu/qss/article/... A 🧵 1/n #AcademicSky #PhDchat #ScientificPublishing #SciPub

🚀 A few weeks ago, we launched Pangaea, a benchmark for geospatial (foundation) models! Focused on dense tasks, Pangea offers a repo to test several models on a global, inclusive and multimodal set of downstream tasks w/ @lebellig.bsky.social @yurujia.bsky.social github.com/yurujaja/pan... 👇

pangaea-bench/pangaea at main · yurujaja/pangaea-bench

Towards Robust Evaluation for Geospatial Foundation Models - yurujaja/pangaea-bench

github.com

I didn't see this already, so I went through my follow list and made a Starter Pack for Women+ in geo & EO - very broadly. I'm reminded how lopsided the gender divide is still in our field when I get 1 woman follower for every 10 men. It's particularly noticeable right now. go.bsky.app/Ay1iTTe

Interested in a #phd in remote sensing of forests? Want to come work with me in Cambridge using drones and ground-based Lidar and photogrammetry? Check out this funding opportunity for the EPSRC Sensor CDT #phd #forests #remotesensing #lidar 🌳🌲🛰️⚡ www.findaphd.com/phds/program...

FindAPhD : EPSRC Centre for Doctoral Training in Sensor Technologies and Applications in an Uncertain World (Sensor CDT) at University of Cambridge

Apply for a PhD: EPSRC Centre for Doctoral Training in Sensor Technologies and Applications in an Uncertain World (Sensor CDT) at University of Cambridge

findaphd.com

It's so fun to see so many people here that I used to interact with in other spaces! There's been a lot of updates to the Open Source Geospatial starter pack. Let me know if you want to be added. go.bsky.app/PGYLmPG

"we have trained more than 50 million neural networks, with more than 150 trillion parameters" 🤯🤯🤯🤯 nianticlabs.com/news/largege... Btw interesting

Building a Large Geospatial Model to Achieve Spatial Intelligence

At Niantic, we are pioneering the concept of a Large Geospatial Model that will use large-scale machine learning to understand a scene and connect it to millions of other scenes globally.

nianticlabs.com

I took the opportunity to make a list of those two starter packs :) bsky.app/profile/did:... I already follow too many people and I need lists to organize myself.

#37 ALISE ALISE (ALigned SITS Encoder) is a model for processing irregular and unaligned Satellite Image Time Series (SITS) ⬆️: great sparse data and label handling ⬇️: would be great to extend the domains (geographical and sensor-related) arxiv.org/abs/2407.08448

There's now so many awesome #geospatial people here it's hard to keep track! 🤩 Here's a thread of some of my favourite starter packs so far - let me know what I've missed! 🛰️ Earth Observation & remote sensing: go.bsky.app/4PMRhNL 🌏 GIS: go.bsky.app/TJ7qQF6 @milos-makes-maps.bsky.social (1/n)

great dataset from great people :)

Nicolas Gonthier@nicaogr.bsky.social · 3y ago

🚨 The FLAIR dataset has been accepted at NeurIPS2023 in the Benchmark & Dataset track. It contains high-resolution aerial imagery with a ground sample distance of 20 cm and over 20 billion individually labeled pixels for precise land-cover classification. arxiv.org/abs/2310.13336

#36 TaxaBind TaxaBind introduces a unified embedding space for 6 data types, solving ecological tasks like species mapping & zero-shot classification ⬆️: robust zero-shot classification ⬇️: what if we want to add/test new tasks? arxiv.org/abs/2411.00683

#1 paper I posted before this year was SatCLIP #SatCLIP learns the implicit representations of the image features that characterize a specific location what I liked most: geo adaptation and per-continent performance paper: arxiv.org/pdf/2311.171...

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lebellig@lebellig.bsky.social · 2y ago

I've initiated a starter pack with researchers working on Deep Learning (SSL, generative models, foundation models, deep learning at scale...) for Earth observation/remote sensing that I know/that I've found on this network! -> Don't hesitate to ping me to be added/removed! 🌏 go.bsky.app/EKVwvde

Hi everyone! 🚀 Happy to be here I am Valerio, from Rome (IT), doing a postdoc at KU Leuven I am working on benchmarking and adapting geospatial foundation models for environmental tasks🌏 Hope to connect with Earth Observation and Deep Learning enthusiasts here! ✨ Follow my #50paperschallenge