9 citations · 10 across the 7 of their papers we have counts for
12 papers
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…
Partial recovery of meter-scale surface weather
Jonathan Giezendanner, Qidong Yang, Eric Schmitt +7
Near-surface atmospheric conditions can differ sharply over tens to hundreds of meters due to land cover and topography, yet this variability is absent from current weather analyse…
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…