Cloud Platforms and Technologies for Big Satellite Data Processing

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dc.contributor.authorKussul, Nataliia
dc.contributor.authorShelestov, Andrii
dc.contributor.authorYailymov, Bohdan
dc.date.accessioned2024-03-01T10:18:24Z
dc.date.available2024-03-01T10:18:24Z
dc.date.issued2023
dc.description.abstractThis paper addresses the problem of processing large volumes of satellite data and compares different cloud platforms for potential solutions. Existing cloud platforms like Google Earth Engine, Amazon Web Services (AWS), and CREODIAS have been used to tackle this challenge. However, this study proposes an optimal pipeline for satellite data processing, taking into account the advantages and limitations of each platform. The specific focus is on solving machine learning problems using satellite data. In the experiment conducted, the effectiveness of each cloud platform was analyzed. It was found that cloud platforms offer benefits such as flexibility, access to computing resources, and parallel processing architectures, leading to increased productivity and cost reduction. CREODIAS, in particular, stands out due to its specialization in satellite data and easy access to various data types, along with tools for data searching and visualization. The experiment demonstrated that tasks, from data loading to classification, were executed fastest on CREODIAS resources. However, AWS performed data classification faster. The availability of its own internal data bucket was a significant advantage of CREODIAS, especially when considering ARD data. These findings contribute to the advancement of AI methodologies and have practical implications for solving satellite monitoring applications.
dc.format.pagerangeP. 303–321
dc.identifier.citationKussul, N. Cloud Platforms and Technologies for Big Satellite Data Processing / Kussul N., Shelestov A., Yailymov B. // Information and Communication Technologies and Sustainable Development. ICT&SD 2022. Lecture Notes in Networks and Systems, vol 809 / In: Dovgyi, S., Trofymchuk, O., Ustimenko, V., Globa, L. (eds). - Cham : Springer, 2023. - P. 303–321.
dc.identifier.doihttps://doi.org/10.1007/978-3-031-46880-3_19
dc.identifier.isbn978-3-031-46879-7
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/65137
dc.language.isoen
dc.publisherSpringer
dc.publisher.placeCham
dc.relation.ispartofInformation and Communication Technologies and Sustainable Development. ICT&SD 2022
dc.subjectSatellite Data Processing
dc.subjectBig Data
dc.subjectGeospatial Information
dc.subjectCloud Computing
dc.subjectReady-To-Use Data
dc.subjectARD Data
dc.subjectMachine Learning
dc.subjectCREODIAS
dc.subjectAWS
dc.subjectGEE
dc.titleCloud Platforms and Technologies for Big Satellite Data Processing
dc.typeBook chapter

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