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Multi-Backend Zonal Statistics Execution with Raven

Gereon Dusella
Haralampos Gavriilidis
Laert Nuhu
Volker Markl
Eleni Tzirita Zacharatou

June 09, 2024

The recent explosion in the number and size of spatial remote sensing datasets from satellite missions creates new opportunities for data-driven approaches in domains such as climate change monitoring and disaster management. These approaches typically involve a feature engineering step that summarizes remote sensing pixel data located within zones of interest defined by another spatial dataset, an operation called zonal statistics. While there exist several spatial systems that support zonal statistics operations, they differ significantly in terms of interfaces, architectures, and algorithms, making it hard for users to select the best system for a specific workload. To address this limitation, we propose Raven, a zonal statistics framework that provides users with a unified interface across multiple execution backends, while facilitating easy benchmarking and comparisons across systems. In this demonstration, we showcase several aspects of Raven, including its multi-backend execution environment, domain-specific declarative language, optimization techniques, and benchmarking capabilities.