RDPro: Distributed Processing of Big Raster Data

Advancements in remote sensing technology allowed for collecting vast amounts of satellite and aerial imagery with up to 1 cm pixel resolutions, stored in raster format crucial for various research fields. However, processing this data poses challenges, including resolving data dependencies when location, resolution, and coordinate systems do not align and managing large datasets within memory constraints. This paper introduces RDPro, a novel Spark-based system that efficiently processes and analyzes large raster datasets. RDPro features a new data model tailored for data dependencies in a distributed, shared-nothing environment, complete with tools for loading and writing raster data. It also optimizes core raster operations within Spark, allowing users to integrate complex data science workflows. Comparative analysis shows RDPro outperforms existing systems by up to two orders of magnitude.