Rapid retrieval strategy for massive remote sensing metadata based on GeoHash coding

With the rapid development of Earth observation technology, satellite data centres have accumulated large amounts of remote sensing data from different spaceborne and airborne sensors. The efficient management and quick retrieval of multisource, massive and heterogeneous remote sensing data in the Big Data age have become increasingly important. In this paper, a spatio-temporal organization model based on GeoHash coding is proposed. First, based on the ISO (International Organization for Standardization) standard, the heterogeneous remote sensing metadata can be converted into a unified format, and the differences in the multisource remote sensing metadata are screened. Then, the GeoHash algorithm is used to encode and convert the latitude and longitude coordinates of the remote sensing metadata to reduce the remote sensing metadata dimensions under space retrieval conditions. Finally, by building an HBase key value model based on GeoHash, a primary key is used to realize the rapid retrieval of massive remote sensing metadata through the simulation of 1500 million remote sensing metadata retrieval experiments; by comparing with the traditional multi-conditional filtering retrievals, the results show that a spatio-temporal organization strategy for remote sensing metadata based on GeoHash coding can effectively improve the efficiency of remote sensing data retrievals.