11 papers
OlmoEarth v1.2: A more efficient family of OlmoEarth models
Gabriel Tseng, Yawen Zhang, Favyen Bastani +8
We present a set of improvements to the OlmoEarth family. These improvements allow us to cut compute costs during training ( reduction in GPU hours required to train ou…
On the Generalizability of Foundation Models for Crop Type Mapping
Yi-Chia Chang, Adam J. Stewart, Favyen Bastani +5
Foundation models pre-trained using self-supervised learning have shown powerful transfer learning capabilities on various downstream tasks, including language understanding, text…
Self-Supervised Multi-Modal World Model with 4D Space-Time Embedding
Lance Legel, Qin Huang, Brandon Voelker +9
We present DeepEarth, a self-supervised multi-modal world model with Earth4D, a novel planetary-scale 4D space-time positional encoder. Earth4D extends 3D multi-resolution hash enc…
Synthesizing Trajectory Queries from Examples
Stephen Mell, Favyen Bastani, Steve Zdancewic +1
Data scientists often need to write programs to process predictions of machine learning models, such as object detections and trajectories in video data. However, writing such quer…
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…
High-Resolution Live Fuel Moisture Content (LFMC) Maps for Wildfire Risk from Multimodal Earth Observation Data
Patrick Alan Johnson, Gabriel Tseng, Yawen Zhang +5
Wildfires are increasing in intensity and severity at an alarming rate. Recent advances in AI and publicly available satellite data enable monitoring critical wildfire risk factors…