Conceptual Design of an XCube-Based Data Cube for Geospatial Applications with Remotely Sensed Imagery

This study presents the conceptual design of a multidimensional data cube for remote sensing applications, which was developed using the open-source XCube framework.Unlike traditional file-based or relational database systems for data storage, and data hubs or catalogs for data services, the proposed structure incorporates spatial, temporal, spectral, and sensor dimensions, facilitating efficient large-scale spatiotemporal analysis.The framework supports the scalable and interoperable management of very highresolution Earth observation (EO) datasets, such as Compact Advanced Satellite (CAS)500-1 imagery, using Zarr-based cloud storage.This study demonstrates interoperability with platforms, including Google Earth Engine (GEE), and focuses on region-specific data sovereignty.Two pilot cases - assessing risk levels in densely built, aged residential areas and analyzing priority areas for road maintenance in forest degradation zones - exemplify the practical utility of this approach in environmental analysis and policy support.The results suggest the potential of an XCube-based data cube to provide a flexible, scalable infrastructure for geospatial analytics.