16 citations · 48 across the 16 of their papers we have counts for
10 papers · 1 filter
How to Embed Matters: Evaluation of EO Embedding Design Choices
Luis Gilch, Isabelle Wittmann, Maximilian Nitsche +3
Earth observation (EO) missions produce petabytes of multispectral imagery, increasingly analyzed using large Geospatial Foundation Models (GeoFMs). Alongside end-to-end adaptation…
Spectral Gaps and Spatial Priors: Studying Hyperspectral Downstream Adaptation Using TerraMind
Julia Anna Leonardi, Johannes Jakubik, Paolo Fraccaro +1
Geospatial Foundation Models (GFMs) typically lack native support for Hyperspectral Imaging (HSI) due to the complexity and sheer size of high-dimensional spectral data. This study…
TerraFlow: Multimodal, Multitemporal Representation Learning for Earth Observation
Nazar Puriy, Johannes Jakubik, Benedikt Blumenstiel +1
We propose TerraFlow, a novel approach to multimodal, multitemporal learning for Earth observation. TerraFlow builds on temporal training objectives that enable sequence-aware lear…
SIMPLER: Efficient Foundation Model Adaptation via Similarity-Guided Layer Pruning for Earth Observation
Víctor Barreiro, Johannes Jakubik, Francisco Argüello +1
Fine-tuning foundation models for Earth Observation is computationally expensive, with high training time and memory demands for both training and deployment. Parameter-efficient m…
Phaedra: Learning High-Fidelity Discrete Tokenization for the Physical Science
Levi Lingsch, Georgios Kissas, Johannes Jakubik +1
Tokens are discrete representations that allow modern deep learning to scale by transforming high-dimensional data into sequences that can be efficiently learned, generated, and ge…
Quantizing Space and Time: Fusing Time Series and Images for Earth Observation
Gianfranco Basile, Johannes Jakubik, Benedikt Blumenstiel +2
We propose a task-agnostic framework for multimodal fusion of time series and single timestamp images, enabling cross-modal generation and robust downstream performance. Our approa…
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…
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…
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…
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…