Scalable AI for Earth and Climate Sciences
Year: 2025
Type: article
Abstract: This tutorial will take participants on a “zero-to-hero” journey through the modern AI toolkit for large-scale Earth and climate data analysis. We will start with the fundamentals of cloud-native Earth Observation (EO) data management, covering spatiotemporal datacubes, object storage, and cloud-optimized formats. Building on this, we will explore scalable computing with Xarray, enabling efficient analysis of large EO datasets. The second part of the tutorial introduces self-supervised learning (SSL) techniques for EO, discussing predictive, contrastive, and generative approaches for multi-modal satellite data. We will then examine sensor-agnostic EO foundation models, their design principles, evaluation strategies, and current challenges, including multimodal integration and temporal modeling. Throughout, we will link conceptual discussions with practical, hands-on exercises, guiding participants from data ingestion to large-scale model training and evaluation. Participants will gain an informed perspective on current tools and methods, along with practical pointers for applying them in their own Earth and climate science projects.
DOI: 10.22032/dbt.67577
Authors: Papoutsis, Ioannis, Multimediazentrum Jena, ELLIS Unit Jena
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7090225521
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