Kussul, NataliiaShelestov, AndriiYailymov, Bohdan2024-03-012024-03-012023Kussul, 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. 303–321.978-3-031-46879-7https://ela.kpi.ua/handle/123456789/65137This 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.enSatellite Data ProcessingBig DataGeospatial InformationCloud ComputingReady-To-Use DataARD DataMachine LearningCREODIASAWSGEEBook chapterP. 303–321https://doi.org/10.1007/978-3-031-46880-3_19