When dealing with continuous data types like elevation and satellite imagery, raster data proves to be more advantageous. Existing techniques convert raster data to vector data for processing, which results in a loss of detail, reduced accuracy, and modification of the original data. This study aims to enhance the processing and analysis of extensive raster datasets while maintaining high precision and minimal data loss. To achieve this objective, the capabilities of GeoTrellis and Docker have been utilized. GeoTrellis can handle large-scale data processing without conversion and supports various raster data storage formats. Docker is employed to distribute computation across a cluster, ensuring that the solution can complete complex tasks efficiently within a reasonable timeframe. Furthermore, the proposed system architecture offers a collection of algorithms that handle data and expedite the output of ad-hoc queries.

The paper has been accepted to the 2nd International Conference on Computing, Communication, and Learning (COCOLE)-2023 to be organized by the National Institute of Technology, Warangal for presentation.