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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. 

Significant increase in high-resolution satellite data requires more productive analysis methods to benefit data scientists. Interactive exploration is essential to productivity since it keeps the user en- gaged by providing quick responses. This paper addresses the pro- gressive zonal statistics problem that given big satellite data, an aggregate function, and a set of query polygons, zonal statistics computes the aggregate function for each query polygon over raster data. Efficiently querying complex polygons, reading high resolu- tion pixels and process multiple polygons simultaneously are three main challenges. This work introduces Viper, an interactive explo- ration pipeline to overcome these challenges and achieve require- ments. Viper uses a raster-vector index to bootstrap the answer with an accurate result in a short time. Then, it progressively refines the answer using a priority processing algorithm to produce the final answer. Experiments on large-scale real data show that Viper can reach 90% accuracy or higher up-to two orders of magnitude faster than baseline algorithms.