Abstract
In the era of rapid data expansion and computer technology development, discrete storage, multiband push and fuzzy query remote sensing data management methods are no longer suitable for the data analysis needs of users, including the needs for long time series, global regions and multidata fusion. After analyzing traditional data management techniques, this paper discusses the existing achievements and development trends of current technologies. This paper aims to solve the problem of data sharing difficulties and organizational inconsistency caused by the use of different formats for the same spatial object. Based on a discrete global grid, this paper studies the blocky division method and coding specification of Google S2 and then accomplishes the layered storage of remote sensing data in HBase. Finally, Kylin is used to build a cube model to discuss the information mining analysis changes in the new data management model. Experiments show that the blocky and layered management schema (BLMS) can realize the unified management of global remote sensing data with multisource, heterogeneous, multiscale, and long-term characteristics and provide accurate data services on demand.
Highlights
During the rapid growth of remote sensing images, various storage centers have accumulated large amounts of image data in different storage formats according to the database system’s stage, technology, economy, physical load and application subjects, making remote sensing data appear diverse, high-dimensional and complex [1]
We provide a description of the core attributes: d) Remote sensing data from different sources are mapped to the corresponding grid level of Google S2 according to the specification, and each level can be mapped at multiple spatial resolutions. e) Remote sensing images are strictly limited to image blocks of uniform size
In this paper, we propose a multilevel spatial tiling method for global remote sensing data based on Google S2, which guarantees the global relevance and multiscale of global spatial information
Summary
During the rapid growth of remote sensing images, various storage centers have accumulated large amounts of image data in different storage formats according to the database system’s stage, technology, economy, physical load and application subjects, making remote sensing data appear diverse, high-dimensional and complex [1]. Different data management methods, such as filesystems, database systems, and hybrid systems of files and databases, are adopted to store and manage the data. Similar to GeoHash, the Google S2 algorithm converts 3D geographic latitude and longitude coordinates into encoded strings similar to URLs, thereby reducing the redundancy generated by traditional methods while ensuring accurate retrieval of target locations. This algorithm is applied to the spatial search service on search engines such as Google Maps, MongoDB and Foursquare. Google S2 uses a Hilbert curve to fill the spatial grid, which makes it dimensionally stable and continuous
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
