Cloud Computing in Remote Sensing : High Performance Remote Sensing Data Processing in a Big data Environment


  • Yassine Sabri
  • Aouad Siham



Remote Sensing, Data integration, Cloud Computing, Big Data


Multi-area and multi-faceted remote sensing (SAR) datasets are widely used due to the increasing demand for accurate and up-to-date information on resources and the environment for regional and global monitoring. In general, the processing of RS data involves a complex multi-step processing sequence that includes several independent processing steps depending on the type of RS application. The processing of RS data for regional disaster and environmental monitoring is recognized as computationally and data demanding.Recently, by combining cloud computing and HPC technology, we propose a method to efficiently solve these problems by searching for a large-scale RS data processing system suitable for various applications. Real-time on-demand service. The ubiquitous, elastic, and high-level transparency of the cloud computing model makes it possible to run massive RS data management and data processing monitoring dynamic environments in any cloud. via the web interface. Hilbert-based data indexing methods are used to optimally query and access RS images, RS data products, and intermediate data. The core of the cloud service provides a parallel file system of large RS data and an interface for accessing RS data from time to time to improve localization of the data. It collects data and optimizes I/O performance. Our experimental analysis demonstrated the effectiveness of our method platform.


[1] Huadong Guo, LizheWang, Fang Chen, et al. Scientific big data and digital earth. Chinese Science Bulletin, 59(35):5066-5073, 2014.

[2] Rissouni Youssef, Moumen Aniss, and Chao Jamal. 2020. Machine Learning and Deep Learning in Remote Sensing and Urban Application: A Systematic Review and Meta-Analysis. In Proceedings of the 4th Edition of International Conference on Geo-IT and Water Resources 2020, Geo-IT and Water Resources 2020 (GEOIT4W-2020). Association for Computing Machinery, New York, NY, USA, Article 18, 1-5.

[3] Weijing Song, Lizhe Wang, Yang Xiang, et al. Geographic spatiotemporal big data correlation analysis via the Hilbert-Huang transformation. Journal of Computer and System Sciences, 89:130 (141), 2017.

[4] Panpan Zheng, Shuhan Yuan, and Xintao Wu. Using Dirichlet Marked Hawkes Processes for Insider Threat Detection. Digital Threats: Research and Practice 0, ja.

[5] Toshihiro Sakamoto, Nhan Van Nguyen, Akihiko Kotera, et al. Detecting temporal changes in the extent of annual ooding within the Cambodia and the Vietnamese Mekong Delta from modis time-series imagery. Remote Sensing of Environment, 109(3):295 - 313, 2007.

[6] Zhang L.F., Chen H., Sun X.J., et al. Designing spatial-temporal-spectral integrated storage structure of multi-dimensional remote sensing images. Journal of Remote Sensing, 21(1):62 73, 2017.

[7] Assis, Luiz Fernando, Gilberto Ribeiro, et al. Big data streaming for remote sensing time series analytics using map-reduce. In XVII Brazilian Symposium on GeoInformatics, 2016.

[8] Yongzhi Huang, Kaixin Chen, Yandao Huang, Lu Wang, and Kaishun Wu. 2021. Vi-liquid: unknown liquid identification with your smartphone vibration. In Proceedings of the 27th Annual International Conference on Mobile Computing and Networking (MobiCom '21). Association for Computing Machinery, New York, NY, USA, 174-187.

[9] Chiwon Lee, Hyunjong Joo, and Soojin Jun. 2021. Social VR as the New Normal? Understanding User Interactions for the Business Arena. Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, Article 420, 1-5.

[10] Lizhe Wang, Weijing Song, and Peng Liu. Link the remote sensing big data to the image features via wavelet transformation. Cluster Computing, 19(2):793-810, Jun 2016.

[11] LizheWang, Jiabin Zhang, Peng Liu, et al. Spectral-spatial multi-featurebased deep learning for hyperspectral remote sensing image classification. Soft Computing, 21(1):213-221, Jan 2017.

[12] Weitao Chen, Xianju Li, Haixia He, et al. Assessing different feature sets' effects on land cover classification in complex surface-mined landscapes by Ziyuan-3 satellite imagery. Remote Sensing, 10(1), 2018.

[13] Myunghee Jung and Sang-Hoon Lee. 2018. Adaptive Reconstruction of Time Series Satellite Data Based on Multi-Periodic Harmonic Analysis. In Proceedings of the 2018 International Conference on Machine Learning and Machine Intelligence (MLMI2018). Association for Computing Machinery, New York, NY, USA, 8-12.

[14] Matthew Rocklin. Dask: Parallel computation with blocked algorithms and task scheduling. In Kathryn Hu and James Bergstra, editors,Proceedings of the 14th Python in Science Conference, pages 130 - 136, 2015.

[15] Erik Thomsen. OLAP Solutions: Building Multidimensional Information Systems. John Wiley Sons, Inc., 2002.

[16] Z. Yijiang. The conceptual design on spatial data cube. In 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), pages 645-648, April 2012.

[17] Juglar Diaz. 2020. Spatio-temporal Conditioned Language Models. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Association for Computing Machinery, New York, NY, USA, 2478.

[18] Tianqin Zhang, Ruimin Lyu, and Zhaolin Yuan. 2021. Visualization of imaginary stroke movement in painting and calligraphy. In ACM SIGGRAPH 2021 Art Gallery (SIGGRAPH '21). Association for Computing Machinery, New York, NY, USA, Article 14, 1-4.

[19] Junqing Fan, Jining Yan, Yan Ma, et al. Big data integration in remote sensing across a distributed metadata-based spatial infrastructure. Remote Sensing, 10(1), 2018.

[20] Jining Yan, Yan Ma, Lizhe Wang, et al. A cloud-based remote sensing data production system. Future Generation Computer Systems, 2017.

[21] Gilberto Camara, Luiz Fernando Assis, et al. Big earth observation data analytics: Matching requirements to system architectures. In Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial '16, pages 1-6, New York, NY, USA, 2016. ACM.

[22] SCIDB. A database management system designed for multidimensional data., 2017.

[23] Apache. Hadoop web site., 2017.

[24] Chun-Yi Liu, Jagadish Kotra, Myoungsoo Jung, and Mahmut Taylan Kandemir. Centaur: A Novel Architecture for Reliable, Low-Wear, High-Density 3D NAND Storage. Proc. ACM Meas. Anal. Comput. Syst.

[25] odc. Open data cube., 2017.

[26] OpenStreetMap. the project that creates and distributes free geographic data for the world., 2017.

[27] UCAR. Netcdf le format and api., 2017.

[28] Stephan Hoyer and Joseph J.Hamman. Xarray: N-d labeled arrays and datasets in python. Journal of Open Research Software, 5(3), 2017.

[29] Weitao Chen, Xianju Li, Haixia He, et al. A review of ne-scale land use and land cover classi cation in open-pit mining areas by remote sensing techniques. Remote Sensing, 10(1), 2018.

[30] Xianju Li, Weitao Chen, Xinwen Cheng, et al. Comparison and integration of feature reduction methods for land cover classi cation with rapideye imagery. Multimedia Tools and Applications, 76(21):23041-23057, Nov 2017.

[31] Xianju Li, Gang Chen, Jingyi Liu, et al. Effects of rapideye imagery's red-edge band and vegetation indices on land cover classification in an arid region. Chinese Geographical Science, 27(5):827- 835, Oct 2017.

[32] Jie Zhang, Jining Yan, Yan Ma, et al. Infrastructures and services for remote sensing data production management across multiple satellite data centers. Cluster Computing, 19(3):1-18, 2016.

[33] Ye Tian, Xiong Li, Arun Kumar Sangaiah, et al. Privacy-preserving scheme in social participatory sensing based on secure multi-party cooperation. Computer Communications, 119:167 178, 2018.

[34] Chen Chen, Xiaomin Liu, Tie Qiu, et al. Latency estimation based on traffic density for video streaming in the internet of vehicles. Computer Communications, 111:176 186, 2017.

[35] CastaƱeda Herrera, L. M.; Campo, W. Y.; Duque-Torres, A. (2021). Video Streaming ServiceIdentification on Software-Defined Networkin,International Journal of Computers Communications & Control, 16(5), 4258, 2021.

[36] Aloudat, M.; Alomari, S.; Qattous, H.; Azzeh, M.; AL-Munaizel, T. (2021). An Interactive Au-tomation for Human Biliary Tree Diagnosis Using Computer Vision,International Journal of Com-puters Communications & Control, 16(5), 4275, 2021.

[37] Daranda A.; Dzemyda G. (2021) Novel Machine Learning Approach for Self-Aware Prediction basedon the Contextual Reasoning, International Journal of Computers Communications & Control, 16(4),4345, 2021.

[38] Song Y.; Deng Y. (2021). Entropic Explanation of Power Set, International Journal of Computers Communications & Control, 16(4), 4413, 2021

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