Automating Earth Observation Analytics Pipelines with Agent Raven
Efficient integration of vector databases, such as those con taining administrative boundaries and land parcels, with re mote sensing images is essential for various Earth Observa tion (EO) applications. Zonal statistics (ZS) offer a powerful tool for this purpose, but their computation remains chal lenging due to fragmented system interfaces, diverse prepro cessing needs, and inconsistent performance across systems. Current methods optimize execution within single systems but lack support for dynamic, cross-system workflows. To address this, we present Agent Raven, the first AI-driven multi-agent system designed to autonomously manage the full lifecycle of ZS computation and deployment. Building on the Raven framework, Agent Raven dynamically selects execution backends, optimizes query pipelines, and adap tively manages workflows based on previous experiments. Our work represents a step forward in intelligent orchestra tion across heterogeneous systems in EO data analytics.
