activity
20222025
most citedPrithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications

22 citations · 50 across the 12 of their papers we have counts for

collaborators

13 papers

cs.CV2025★ 1 cited

Hyperspectral Vision Transformers for Greenhouse Gas Estimations from Space

Ruben Gonzalez Avilés, Linus Scheibenreif, Nassim Ait Ali Braham +8

Hyperspectral imaging provides detailed spectral information and holds significant potential for monitoring of greenhouse gases (GHGs). However, its application is constrained by l…

cs.CV2025

Fine-tune Smarter, Not Harder: Parameter-Efficient Fine-Tuning for Geospatial Foundation Models

Francesc Marti-Escofet, Benedikt Blumenstiel, Linus Scheibenreif +2

Earth observation (EO) is crucial for monitoring environmental changes, responding to disasters, and managing natural resources. In this context, foundation models facilitate remot…

cs.CV2025

TerraMesh: A Planetary Mosaic of Multimodal Earth Observation Data

Benedikt Blumenstiel, Paolo Fraccaro, Valerio Marsocci +8

Large-scale foundation models in Earth Observation can learn versatile, label-efficient representations by leveraging massive amounts of unlabeled data. However, existing public da…

cs.CV2025

TerraMind: Large-Scale Generative Multimodality for Earth Observation

Johannes Jakubik, Felix Yang, Benedikt Blumenstiel +13

We present TerraMind, the first any-to-any generative, multimodal foundation model for Earth observation (EO). Unlike other multimodal models, TerraMind is pretrained on dual-scale…

cs.CV2025★ 3 cited

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…

eess.SP2025★ 5 cited

Lossy Neural Compression for Geospatial Analytics: A Review

Carlos Gomes, Isabelle Wittmann, Damien Robert +24

Over the past decades, there has been an explosion in the amount of available Earth Observation (EO) data. The unprecedented coverage of the Earth's surface and atmosphere by satel…

eess.IV2025

Multispectral to Hyperspectral using Pretrained Foundational model

Ruben Gonzalez, Conrad M Albrecht, Nassim Ait Ali Braham +6

Hyperspectral imaging provides detailed spectral information, offering significant potential for monitoring greenhouse gases like CH4 and NO2. However, its application is constrain…

cs.CV2024★ 22 cited

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…

cs.AI2024★ 1 cited

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…

cs.SE2023

Variational Exploration Module VEM: A Cloud-Native Optimization and Validation Tool for Geospatial Modeling and AI Workflows

Julian Kuehnert, Hiwot Tadesse, Chris Dearden +7

Geospatial observations combined with computational models have become key to understanding the physical systems of our environment and enable the design of best practices to reduc…

cs.CV2023★ 16 cited

Foundation Models for Generalist Geospatial Artificial Intelligence

Johannes Jakubik, Sujit Roy, C. E. Phillips +30

Significant progress in the development of highly adaptable and reusable Artificial Intelligence (AI) models is expected to have a significant impact on Earth science and remote se…

cs.DC2022★ 1 cited

Climate Impact Modelling Framework

Blair Edwards, Paolo Fraccaro, Nikola Stoyanov +4

The application of models to assess the risk of the physical impacts of weather and climate and their subsequent consequences for society and business is of the utmost importance i…

cs.CV2022★ 1 cited

Deep Temporal Interpolation of Radar-based Precipitation

Michiaki Tatsubori, Takao Moriyama, Tatsuya Ishikawa +5

When providing the boundary conditions for hydrological flood models and estimating the associated risk, interpolating precipitation at very high temporal resolutions (e.g. 5 minut…