MSTGI: a multi-scale spatio-temporal grid index model for remote-sensing big data retrieval

Abstract

ABSTRACT To promote the transformation of remote sensing (RS) data into geoscience knowledge, it is necessary to provide better data discovery capabilities, especially when large amounts of RS data have been accumulated. Spatio-temporal range query is one of them to enable data discovery on RS images by the spatio-temporal range. Although existing RS image indexing methods are suitable for data discovery based on spatio-temporal range queries, they do not fully consider the problem of low data retrieval efficiency when the temporal and spatial range scales are unbalanced. To address this problem, we propose a multi-scale spatio-temporal grid index model (MSTGI). First, we divide the time dimension into three levels according to the granularity of year, month and day, and build an independent grid index structure for the object at different levels. Second, we design an adaptive hierarchical indexing strategy to perform parallel retrieval in an appropriate combination of partitions. MSTGI linearizes the grid obtained after global geospatial subdivision using Hilbert curves. Our experimental results, obtained on the Landsat series satellite dataset, reveal that the proposed method improves query efficiency levels by approximately 12.582% and 87.754% compared with GeoSOT-ST and AGMC at various spatial scales, respectively, and reaches 100% recall.