ABSTRACT
This chapter describes the spatial-feature remote sensing data cube (SRSDC), a data cube whose goal is to deliver a spatial-feature-supported, efficient, and scalable multidimensional data analysis system to handle the large-scale RS data. It presents an architectural overview of the SRSDC. The SRSDC provides spatial feature repositories to store and manage the vector feature data, and a feature translation to transform the spatial feature information to a query operation. The chapter explains the design and implementation of a feature data cube and distributed execution engine in the SRSDC. It utilizes the long time-series remote sensing production process and analysis as examples to evaluate the performance of a feature data cube and distributed execution engine. As a new strategic resource for human beings, big data has become a strategic highland in the era of knowledge economy. The core knowledge discovery methods include supervised learning methods data analysis supervised learning, unsupervised learning methods data analysis unsupervised learning, and their combinations and variants.
