22 citations · 36 across the 6 of their papers we have counts for
8 papers
Multimodal-Multiresolution Foundation Model for Lunar Remote Sensing
Paolo Fraccaro, Gabby Nyirjesy, Daniela Szwarcman +19
We present a multimodal foundation model for lunar remote sensing, pretrained from scratch on SomBench, a geographically partitioned corpus of nearly two million co-registered tile…
SomBench: Benchmark Dataset for Advancing Machine Learning in Lunar Science
Himanshu Patil, Gabby Nyirjesy, Rachel A. Slank +19
Lunar orbital missions, such as Lunar Reconnaissance Orbiter, Kaguya/SELENE, Gravity Recovery and Interior Laboratory, and Lunar Prospector, among others, provide rich multi-instru…
TerraTorch: The Geospatial Foundation Models Toolkit
Carlos Gomes, Benedikt Blumenstiel, Joao Lucas de Sousa Almeida +7
TerraTorch is a fine-tuning and benchmarking toolkit for Geospatial Foundation Models built on PyTorch Lightning and tailored for satellite, weather, and climate data. It integrate…
Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications
Daniela Szwarcman, Sujit Roy, Paolo Fraccaro +33
This paper presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2 million global time…
Prithvi WxC: Foundation Model for Weather and Climate
Johannes Schmude, Sujit Roy, Will Trojak +26
Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing numbe…
Evaluating the transferability potential of deep learning models for climate downscaling
Ayush Prasad, Paula Harder, Qidong Yang +4
Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding and adapting to climate change at region…
Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation
Michal Muszynski, Levente Klein, Ademir Ferreira da Silva +13
Global vegetation structure mapping is critical for understanding the global carbon cycle and maximizing the efficacy of nature-based carbon sequestration initiatives. Moreover, ve…
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network
Takuya Kurihana, Kyongmin Yeo, Daniela Szwarcman +4
To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the…