TerraMind vs. THOR: A comparative analysis of ESA’s Geospatial Foundation Models

This study presents a comparative analysis of two diverse Geospatial Foundation Models (GFMs) developed by consortia under the European Space Agency (ESA): THOR and TerraMind. THOR introduces a compute-adaptive architecture designed to handle heterogeneous sensors and variable patch sizes. This enables flexible compute–accuracy trade-offs and high performance in limited training data regimes. It is also the first GFM to extensively include Sentinel-1, -2, and -3 data. TerraMind, in contrast, is a multimodal GFM with both discriminative and generative capabilities, pretrained with a dual‑scale scheme that fuses token‑level context and pixel‑level detail, enabling any‑to‑any cross‑modal generation and Thinking‑in‑Modalities (TiM) to infer missing modalities during fine‑tuning and inference. The cross-comparison, aimed to understand the level of maturity of European technologies in AI4EO, covers a collection of Earth Observation use cases provided by the two consortia, encompassing several tasks (segmentation, change detection, and classification), across diverse and overlooked domains, including climate disaster analysis, methane leak detection, forest biomass monitoring, and sea ice mapping. To ensure consistent preprocessing and evaluation of the two models and use cases, we benchmarked them in two very widespread and acknowledged framework: PANGAEA and TerraTorch. The analysis focuses on task coverage, architectural capabilities, and performance metrics, highlighting differences in adaptability, modality integration, and downstream application effectiveness. Results provide insights into the strengths and limitations of current GFMs for various scenarios, making it possible to grasp insights on different GFMs approaches, not limited to THOR and TerraMind.