12 papers
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…
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…
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…
TerraCodec: Compressing Optical Earth Observation Data
Julen Costa-Watanabe, Isabelle Wittmann, Benedikt Blumenstiel +1
Earth observation (EO) satellites produce massive streams of multispectral image time series, posing pressing challenges for storage and transmission. Yet, learned EO compression r…
SSL4EO-S12 v1.1: A Multimodal, Multiseasonal Dataset for Pretraining, Updated
Benedikt Blumenstiel, Nassim Ait Ali Braham, Conrad M Albrecht +2
This work presents SSL4EO-S12 v1.1, a multimodal, multitemporal Earth Observation dataset designed for pretraining large-scale foundation models. Building on the success of SSL4EO-…
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…