# RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
## 5 Conclusions  
With the rapid development of remote sensing technology, remote sensing data keep growing explosively, and play a more and more important role in environmental protection, economic development, etc. However, managing such huge amounts of remote sensing data is still a big challenge. Besides the large volume, big remote sensing data have many specific features, such as multi-scale, high-dimension, spatiotemporal pattern, etc. Moreover, metadata of remote sensing images from different sources vary greatly. Traditional storage systems are inappropriate to be used directly for big remote sensing data management.  
Faced with these challenges, we designed and implemented the remote sensing image management system (RSIMS), a new distributed remote sensing data storage system. RSIMS adopts a modular and layered architecture, which is composed of RSIAPI, RSIMeta and RSIData modules. Structured text metadata and unstructured binary file data of remote sensing images are managed by RSIMeta and RSIData separately. RSIMeta stores all the metadata extracted from different remote sensing images according to the uniform storage models, and then builds a distributed spatial index based on Hilbert curve for better spatiotemporal retrieval performance. Up to now, RSIMeta has integrated about 100 million remote sensing images and can retrieve millions of remote sensing images intersected witha large geospatial region within 10 seconds; RSIData is a distributed remote sensing image file storage system with large scalable capacity and high I/O performance. In order to be compatible with GDAL programs, RSIData provides two types of I/O interfaces, which can be used to access remote sensing image files directly stored in RSIData by most of popular related tools and software. On top of them, RSIDataI provides a set of uniform Python APIs for remote sensing applications, hiding the complex inner storage structures from the users. Finally, comparative experiments prove the high and stable performance of RSIMS for large-scale remote sensing image storage, access, and retrieval.  
Some work has been left for the future due to a lack of time. First, we would conduct more research on remote sensing data formats and standards, and then improve the metadata models to make the data integration process more automatic and accurate. Besides that, we would continue to extend and optimize I/O interfaces of RSIMS to provide better support for remote sensing applications and combine with popular distributed computing systems to achieve better process ana analysis efficiency.  
Conceptualization, X.Z., X.W. and Y.Z.; methodology X.Z., X.W. and Y.Z.; software, X.Z. and X.W.; validation X.Z., X.M.; writing--original draft preparation, X.Z.; writing--review and editing, X.Z., X.W. and Y.Z.; project administration, X.W. and Y.Z.; validation, Y.Z., Q.L., J.Z. and X.M.; Visualization, Q.L. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Key Research Program of Frontier Sciences, CAS, and grant number ZDBS-LY-DQC016, and also supported in part by the National Natural Science Foundation of China (NSFC) under grant 61836013 and in part by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19020103).  
Not applicable.  
The data presented in this study are available on request from the author.  
We thank GSCLOUD ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn)), accessed on 11 April 2021) for experimental data support and technical solutions inspiration.  
The authors declare no conflict of interest.