Multi-Backend Zonal Statistics Execution with Raven

Gereon Dusella, Haralampos Gavriilidis, Laert Nuhu, Volker Markl, Eleni Tzirita Zacharatou

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Abstract

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. Although several spatial systems 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. This demonstration showcases Raven 's multi-backend execution environment, domain-specific declarative language, optimization techniques, and benchmarking capabilities.
Original languageEnglish
Title of host publicationCompanion of the 2024 International Conference on Management of Data
Number of pages4
PublisherAssociation for Computing Machinery
Publication date9 Jun 2024
Pages532–535
DOIs
Publication statusPublished - 9 Jun 2024
SeriesSIGMOD/PODS '24

Keywords

  • big spatial data
  • parcel-based classification
  • satellite imagery
  • spatial join
  • unified spatial data analytics
  • zonal statistics
  • earth observation

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