arXiv:2511.11706v3 Announce Type: replace 
Abstract: Earth observation (EO) foundation models have emerged as an effective approach to derive latent representations of the Earth system from various remote sensing sensors. These models produce embeddings that can be used as analysis-ready datasets, enabling the modelling of ecosystem dynamics without extensive sensor-specific preprocessing. However, existing models typically operate at fixed spatial or temporal scales, limiting their use for ecological analyses that require both fine spatial detail and high temporal fidelity. To overcome these limitations, we propose a representation learning framework that integrates different EO modalities into a unified feature space at high spatio-temporal resolution. We introduce the framework using Sentinel-1 and Sentinel-2 data as representative modalities. Our approach produces a latent space at native 10 m resolution and the temporal frequency of cloud-free Sentinel-2 acquisitions. Each sensor is first modeled independently to capture its sensor-specific characteristics. Their representations are then combined into a shared model. This two-stage design enables modality-specific optimisation and easy extension to new sensors, retaining pretrained encoders while retraining only fusion layers. This enables the model to capture complementary remote sensing data and to preserve coherence across space and time. Qualitative analyses reveal that the learned embeddings exhibit high spatial and semantic consistency across heterogeneous landscapes. Quantitative evaluation in modelling Gross Primary Production reveals that they encode ecologically meaningful patterns and retain sufficient temporal fidelity to support fine-scale analyses. Overall, the proposed framework provides a flexible, analysis-ready representation learning approach for environmental applications requiring diverse spatial and temporal resolutions.

تم اقتراح إطار جديد لتعلم التمثيلات متعددة الوسائط لتحسين النمذجة البيئية باستخدام بيانات مراقبة الأرض. يدمج هذا الإطار بين أنماط مختلفة من الاستشعار عن بعد في فضاء ميزات موحد، محققًا دقة مكانية وزمنية عالية. من خلال استخدام بيانات Sentinel-1 و Sentinel-2، ينتج الإطار مساحة كامنة بدقة أصلية تبلغ 10 أمتار، مما يسهل التحليلات البيئية دون الحاجة إلى معالجة مسبقة واسعة.

Se ha propuesto un nuevo marco para el aprendizaje de representaciones multimodales que mejora la modelización ambiental utilizando datos de observación de la Tierra. Este marco integra diversas modalidades de teledetección en un espacio de características unificado, logrando una alta resolución espacio-temporal. Al utilizar datos de Sentinel-1 y Sentinel-2, el marco genera un espacio latente a una resolución nativa de 10 metros, facilitando análisis ecológicos sin un extenso preprocesamiento.

Un nouveau cadre d'apprentissage de représentation multimodale a été proposé pour améliorer la modélisation environnementale à l'aide de données d'observation de la Terre. Ce cadre intègre diverses modalités de télédétection dans un espace de caractéristiques unifié, atteignant une haute résolution spatio-temporelle. En utilisant les données Sentinel-1 et Sentinel-2, le cadre génère un espace latent à une résolution native de 10 mètres, facilitant les analyses écologiques sans prétraitement extensif.

A new framework for multimodal representation learning has been proposed to enhance environmental modeling using Earth observation data. This framework integrates various remote sensing modalities into a unified feature space, achieving high spatio-temporal resolution. By utilizing Sentinel-1 and Sentinel-2 data, the framework generates a latent space at a native resolution of 10 meters, facilitating ecological analyses without extensive preprocessing.

Context-Aware Multimodal Representation Learning for Spatio-Temporally Explicit Environmental Modelling

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Context-Aware Multimodal Representation Learning for Spatio-Temporally Explicit Environmental Modelling

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