GeOPipe: A Pipeline for Optimized Retrieval and Storage of Multispectral Satellite Imagery

Efficient management of large-scale geospatial datasets is crucial for timely and effective environmental monitoring. GeOPipe integrates advanced spatial indexing using hashing with selective compression techniques to optimize the retrieval and storage of Sentinel-2 satellite imagery within specific geographic areas. This research demonstrates how hashing enables constant-time (O(1)) data retrieval, significantly outperforming traditional spatial indexing methods like R-trees. By compressing data only within designated geographic boundaries, GeOPipe enhances data access speed and reduces storage requirements without compromising image quality. Experimental results show that GeOPipe improves retrieval times by achieving a retrieval complexity of O(1) and decreases storage needs by 45-50% compared to conventional systems. Additionally, GeOPipe's optimized data handling provides a streamlined input pipeline for deep learning frameworks, improving the performance of tasks like semantic segmentation and land cover classification. This capability is essential for applications requiring rapid and accurate environmental assessments. GeOPipe represents a significant advancement in satellite imagery analysis, offering an efficient and scalable solution for managing environmental data and enhancing the integration of geospatial datasets in machine learning workflows.