Large-Scale Open Remote Sensing Images Map Rendering and Cache Optimization
Remote sensing data are witnessing a rapid growth,with the volume of publicly available remote sensing images expected to reach the Exabyte(EB)scale.However,diverse product types,complex internal structures,and large file sizes pose many challenges to the discovery and sharing of large-scale open remote sensing data.Online maps provide an efficient and effortless method for users to perform online visualization analyses of massive cloud remote sensing images without downloading.Given the low rendering efficiency of map tiles and the poor adaptability of remote sensing data in traditional map technologies,this paper introduces TiMap,an efficient big web map platform for large-scale,multi-source remote sensing data.By leveraging the spatiotemporal attributes and user access behavior characteristics of remote sensing data on the GSCloud platform,TiMap enhances the performance and adaptability of remote sensing data rendering.TiMap comprises a distributed map tile rendering module(TiRender)and a distributed map tile cache module(TiCache).TiRender transforms map tile rendering operations into synchronous real-time rendering and asynchronous batch pre-rendering tasks,leveraging multi-node parallel computing for the rapid response of map tiles.The map tiles generated by TiRender are then cached and managed by TiCache.TiCache distributes map tiles over multi-cache nodes based on layer diversity,which maintains the workload balance of all cache nodes.Experiments demonstrate that TiRender and TiCache outperform other similar technologies.Their collaborative functionality enables TiMap to respond quickly to large-scale map tile requests within 100 ms.
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