Thematic Tile Set Organization and Updating based on Global Subdivision Grid Model

The exponential growth of Earth observation (EO) data offers unprecedented support for spatial analysis and decision-making. However, it also presents significant challenges in data organization, management, and utilization. Organizing heterogeneous EO image data into analysis-ready tiles can significantly enhance data usage efficiency. However, existing methods lack customized approaches tailored to specific themes as well as effective update mechanisms. To address these challenges, this study proposes a thematic tile data cube model that is grounded in themes and utilizes Global Subdivision Grids within an adaptive data cube framework. The model employs a finite state machine approach for updating the tiles, allowing for the organization and updating of these tiles to precisely meet specific thematic needs, aiding in creating analysis-ready datasets. Using the prediction of cold-water coral distribution in the North Atlantic as a case study, this research demonstrates the standardization and tiling of environmental variable data as well as the simulation of batch data updates. Predictive analysis using the random forest model on two thematic tile data cubes shows a significant improvement in prediction accuracy following updates. The results validate the effectiveness of the thematic tile data cube model in data structuring and updating. This study presents a novel approach for the efficient organization and updating of image tile data, providing users with customized analysis-ready tile datasets and continuous updates.