Raster data represents a fundamental form of geospatial information, with satellite imagery serving as a prominent example. Domain scientists across multiple fields [3, 5, 15, 18, 23] rely extensively on raster data for their research. Currently, over 900 Earth observation satellites are active, with projections indicating this number will exceed 2,300 by 2032 [4]. Recent advances in deep learning [12] and foundation models [13, 17] have increasingly focused on satellite imagery applications, addressing critical tasks such as land cover classification and image segmentation using data from platforms like Sentinel-2 and Landsat 8. NASA projects that its Earth Science data will exceed 600 petabytes by 2030 [4], highlighting the exponential growth in data volume and the corresponding computational demands. As both the volume and complexity of raster data continue to increase, there is an urgent need for scalable processing frameworks. This paper summarizes the work on a large-scale raster data processing framework, including its optimization strategies, visualization, real-world applications, and future directions for benchmarking foundation models.

Raster data plays a crucial role in Earth observation and scientific datasets across various domains and industrial applications, such as agriculture, weather forecasting, and disaster monitoring. Meanwhile, the rapidly increasing size of high-resolution raster imagery requires large scale data processing. Traditional single-machine approaches often fail in querying terabyte-scale datasets. Efforts on distributed systems that address this limitation is an active ongoing research topic. This tutorial aims to engage the SIGSPATIAL community with the key challenges and opportunities in large scale raster data processing, which consists of seven parts. Part I provides the background of parallel and distributed systems. Part II provides the necessary background and motivation for big raster data. Part III introduces how users query raster datasets. Part IV summarizes existing system architectures. Part V discusses core management principles, focusing on raster data models, loading, and writing. Part VI presents an in-depth exploration of raster query processing techniques across various systems. Part VII shows real-world applications. Finally, Part VIII concludes the tutorial by outlining current open research challenges in the field.

Remote sensing data has become increasingly important for domain scientists from diverse areas including agriculture. Traditional data analytics methods cannot scale to big-data as they normally run on a single machine. This led to new systems that can process satellite data on distributed environments. However, directly translating traditional query processing pipelines to distributed systems is still challenging due to the complexity of raster data processing, such as varying spatial and temporal resolutions, computational overhead from iterative operations, and dealing with excessive data volumes. This paper introduces FieldSAT, a real-life case study of scaling query pipelines from the agriculture field using a Spark-based raster processing system named RDPro. This work focuses on the BAITSSS model, which is a complex, yet very important, model for estimating evapotranspiration from raster data on large scale. The paper goes beyond straightforward implementation of the model and introduces key optimization techniques and lessons learned that can be broadly applied to similar applications. The optimizations introduced in FieldSAT include: managing prolonged Spark DAGs and dependency chains, minimizing shuffle volumes, implementing raster interpolation methods for extreme resolution differences, and selective data persistence to eliminate redundant processing. Our approach demonstrates how distributed frameworks can be effectively adapted to handle complex scientific analysis pipelines used by domain scientists while maintaining computational efficiency.

As the global demand for sustainable food production intensifies, precision agriculture has become crucial in meeting this challenge. Traditional methods of soil data collection are often laborious and hazardous, while the complexity of processing extensive satellite data remains prohibitive for many farmers. This demonstration introduces FutureFarmNow, a novel mobile application that addresses these challenges by seamlessly integrating satellite imagery with a user-friendly mobile interface. It simplifies access to high-resolution soil data for the Colorado River Basin, enabling farmers to make informed decisions without the need for complex data processing. The application's innovative backend utilizes lightweight index facilities for efficient large-scale data analysis, bridging the gap between farmers and data scientists.

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 engaged by providing quick responses. This paper addresses the progressive 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 resolution pixels and process multiple polygons simultaneously are three main challenges. This work introduces Viper, an interactive exploration pipeline to overcome these challenges and achieve requirements. 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.