5 citations · 11 across the 12 of their papers we have counts for
6 papers · 1 filter
OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation
Henry Herzog, Favyen Bastani, Yawen Zhang +23
Earth observation data presents a unique challenge: it is spatial like images, sequential like video or text, and highly multimodal. We present OlmoEarth: a multimodal, spatio-temp…
Deploying Geospatial Foundation Models in the Real World: Lessons from WorldCereal
Christina Butsko, Kristof Van Tricht, Gabriel Tseng +6
The increasing availability of geospatial foundation models has the potential to transform remote sensing applications such as land cover classification, environmental monitoring,…
DataS^3: Dataset Subset Selection for Specialization
Neha Hulkund, Alaa Maalouf, Levi Cai +15
In many real-world machine learning (ML) applications (e.g. detecting broken bones in x-ray images, detecting species in camera traps), in practice models need to perform well on s…
A Woman with a Knife or A Knife with a Woman? Measuring Directional Bias Amplification in Image Captions
Rahul Nair, Bhanu Tokas, Hannah Kerner
When we train models on biased datasets, they not only reproduce data biases, but can worsen them at test time - a phenomenon called bias amplification. Many of the current bias am…
Galileo: Learning Global & Local Features of Many Remote Sensing Modalities
Gabriel Tseng, Anthony Fuller, Marlena Reil +7
We introduce a highly multimodal transformer to represent many remote sensing modalities - multispectral optical, synthetic aperture radar, elevation, weather, pseudo-labels, and m…
How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models?
Mirali Purohit, Gedeon Muhawenayo, Esther Rolf +1
Foundation models have made rapid advances in many domains including Earth observation, where Geospatial Foundation Models (GFMs) can help address global challenges such as climate…