The organizing committee invites the submission of full papers for presentation at the EO4CUR 2026 workshop.

We welcome original research contributions addressing the use of Earth Observation data for climate adaptation and urban resilience, with a particular emphasis on big data analytics, machine learning, and scalable geospatial processing.

EO4CUR welcomes original research contributions, applied papers, benchmark studies, dataset papers, system demonstrations, and position papers on, but not limited to
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•Big EO data infrastructure: scalable cloud pipelines, STAC/COG/Zarr-based workflows, distributed EO data management, indexing, compression, and retrieval

•Distributed and stream processing: real-time and near-real-time EO analytics, streaming architectures for satellite data, edge-cloud continuum for EO

•Multi-sensor and multimodal fusion: multi-resolution, multi-temporal, and cross-modal fusion of optical, SAR, hyperspectral, and thermal data

•Foundation models and self-supervised learning for EO: large-scale pre-training on EO archives, transfer learning, multi-modal EO transformers

•Resilient mobility and transportation: EO-driven analytics for disaster-resilient transit, evacuation routing, and monitoring of critical transport infrastructure

•Federated and privacy-preserving learning: federated learning across distributed EO data silos, differential privacy for geospatial AI

•Benchmarks and reproducibility: curated EO datasets, open-source toolchains, FAIR data principles, standard evaluation protocols for EO tasks

•Climate and urban risk analytics: EO-based flood, wildfire, windstorm, landslide, and urban heat island mapping; exposure and vulnerability modeling; infrastructure risk assessment

•Causal and scenario modeling: what-if analyses, digital twins, scenario-driven resilience assessment

•Decision support and early-warning systems: EO-driven dashboards, early-warning pipelines, evidence-based adaptation planning

•Integration with heterogeneous sources: fusion of EO with in-situ sensor networks, mobility traces, OpenStreetMap, and administrative/census data

•Explainable and trustworthy AI for EO: uncertainty quantification, model interpretability, bias in EO-based models

•Operational case studies: industrial deployments, public-sector pilots, and lessons learned from production-scale EO systems