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A service-oriented framework for remote sensing big data processing

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Abstract

In recent years, one of the biggest concerns of researchers has been environmental knowledge. This concern can be resolved by collecting and processing remote sensing data in the shortest possible time and cost with the highest accuracy and efficiency. In remote sensing, various types of satellite data are processed for different purposes and applications. In this process, data storage and processing methods, resource management, scalability, performance improvement, and efficiency are among the issues and challenges in this scope. This paper presents a service-oriented framework using big data and parallel processing in remote sensing to address these challenges. The proposed framework provides scalability, flexibility, and generalization without dependency on specific data or processing techniques. In addition, it provides reasonable results to quality criteria such as response time, efficiency, and performance. The evaluation results of the proposed framework show the effectiveness of the framework for various types of analysis of remote sensing data with acceptable accuracy.

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Authors and Affiliations

  1. Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

    Roohollah Enayati, Reza Ravanmehr & Vahe Aghazarian

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  1. Roohollah Enayati
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  2. Reza Ravanmehr
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  3. Vahe Aghazarian
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Contributions

Roohollah Enayati: Concept, Design, Methodology, Implementation, Evaluation. Reza Ravanmehr: Concept, Verification, Validation, Editing. Vahe Aghazarian: Consulting, Editing.

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Correspondence to Reza Ravanmehr.

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Communicated by: H. Babaie

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Enayati, R., Ravanmehr, R. & Aghazarian, V. A service-oriented framework for remote sensing big data processing. Earth Sci Inform 16, 591–616 (2023). https://doi.org/10.1007/s12145-022-00900-w

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