4 papers
Scaling Laws for Geospatial Foundation Models: A case study on PhilEO Bench
Nikolaos Dionelis, Riccardo Musto, Jente Bosmans +7
Foundation Models (FMs) have achieved state-of-the-art performance across domains by leveraging large-scale pretraining. In Earth Observation (EO), the availability of petabyte-sca…
CARE: Confidence-Aware Regression Estimation of building density fine-tuning EO Foundation Models
Nikolaos Dionelis, Jente Bosmans, Nicolas Longépé
Performing accurate confidence quantification and assessment in pixel-wise regression tasks, which are downstream applications of AI Foundation Models for Earth Observation (EO), i…
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…
Evaluating and Benchmarking Foundation Models for Earth Observation and Geospatial AI
Nikolaos Dionelis, Casper Fibaek, Luke Camilleri +3
When we are primarily interested in solving several problems jointly with a given prescribed high performance accuracy for each target application, then Foundation Models should fo…