Abstract
Over the past few years, Earth Observation (EO) has been continuously generating much spatiotemporal data that serves for societies in resource surveillance, environment protection, and disaster prediction. The proliferation of EO data poses great challenges in current approaches for data management and processing. Nowadays, the Array Database technologies show great promise in managing and processing EO Big Data. This paper suggests storing and processing EO data as multidimensional arrays based on state-of-the-art array database technologies. A multidimensional spatiotemporal array model is proposed for EO data with specific strategies for mapping spatial coordinates to dimensional coordinates in the model transformation. It allows consistent query semantics in databases and improves the in-database computing by adopting unified array models in databases for EO data. Our approach is implemented as an extension to SciDB, an open-source array database. The test shows that it gains much better performance in the computation compared with traditional databases. A forest fire simulation study case is presented to demonstrate how the approach facilitates the EO data management and in-database computation.
Highlights
Earth Observation is a series of activities for collecting, managing, processing, analyzing, and presenting the physical, chemical, and biological information pertaining to the Earth system using remote sensing or other measurement techniques
Through SciDB-Py Application Programming Interfaces (APIs), data can be retrieved by the given geographic extent, all the data from different observation themes are converted into Python arrays
This paper presents how to leverage array database technologies for Earth Observation (EO) data management and processing
Summary
Earth Observation is a series of activities for collecting, managing, processing, analyzing, and presenting the physical, chemical, and biological information pertaining to the Earth system using remote sensing or other measurement techniques. The most obvious problem is that it is not easy to retrieve and query the information needed To solve this problem, there have been some efforts to provide a more productive environment for EO data storage and processing by leveraging the state-of-the-art multidimensional array databases and High-Performance Computing (HPC) technologies [6]. An array database is designed and implemented as a common database service offering flexible and scalable storage and retrieval on large volumes of multidimensional array data, such as sensor, image, simulation or statistics data [7,8]. It has attracted extensive attention from academic and industry data scientists [7,9,10].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
