COMPARATIVE ANALYSIS OF CLOUD SERVICES FOR GEOINFORMATION DATA PROCESSING
DOI:
https://doi.org/10.31649/1999-9941-2023-57-2-50-57Keywords:
cloud technologies, geographic information systems, geographic information data processing, cloud services, image analysis, satellite data processing, machine learning, cost, security, scalability, testing and validation, efficiency, performance, recommendationsAbstract
Abstract. The article is devoted to a comparative analysis of cloud services for processing geographic data. It describes in detail the services - Google Cloud, Amazon Web Services and Microsoft Azure - that provide tools for storing, processing and analyzing large amounts of geographic data. The article also describes the parameters of geoinformation services, the access algorithm, and examples of program code for processing satellite data. The article describes such opportunities and limitations of using cloud services as automation, security and scalability. The conclusions and recommendations for further development of geographic information systems based on cloud services are provided. Services. Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) offer a variety of geodata storage solutions. These solutions include object storage, such as Amazon S3, Azure Blob Storage, and Google Cloud Storage, as well as geospatial databases, such as Amazon RDS, Azure Cosmos DB, and Google Cloud Firestore. In addition, each of these services provides a set of services for analyzing and processing geographic information data. For example, AWS offers services such as Amazon Athena, Amazon Redshift, and AWS Glue, which allow you to run SQL queries, conduct analytics, and integrate geodata with other services. Azure offers services such as Azure SQL Database, Azure Databricks, and HDInsight, which provide capabilities for processing and analyzing geographic data. GCP also provides services such as BigQuery, Dataflow, and Dataproc, which allow you to perform analytical operations and process large amounts of geodata. Support for integration with various geo-tools is important for analysis, such as AWS, Amazon Location Service, Amazon Ground Truth, and Amazon Rekognition, which allow you to work with geodata at different levels of complexity. Azure has Azure Maps, which provides services for geocoding, routing, and visualization of geodata. GCP also offers Google Maps Platform, which provides extensive integration with geographic technologies such as routing, geocoding, and map visualization. All these processes will allow for more efficient data processing.
References
Carillo, K. D., & White, D. (2020). Cloud Computing: Concepts, Technology, and Architecture. Morgan Kaufmann.
Kuo, M. F. (2020). Geographic Information Science: Fundamentals and Applications. CRC Press.
Chen, M., & Zhang, Y. (2020). Cloud Computing for Geospatial Data Analysis and Applications. Springer.
Kolodziej, J., & González-Vélez, H. (Eds.). (2020). Cloud Computing for Science and Engineering. Wiley.
Li, X., & Karimi, H. A. (2020). Geospatial Data Science Techniques and Applications. Springer.
Fan, Y., et al. (2021). Geospatial Machine Learning for Urban Research and Planning. Springer.
Ahmed, N., & Sheng, Q. Z. (2021). Security and Privacy in Cloud Computing for Geospatial Data Services. Springer.
Deng, Z., et al. (2020). Cloud Computing for Geoengineering: Concepts, Applications, and Perspectives. Springer.
Laplante, P. A. (2020). Requirements Engineering for Software and Systems. CRC Press.
Vögler, M., & Gartner, G. (2021). User-Centric Requirements Engineering in the Cloud. Springer.
Li, M., & Zhang, Z. (2020). Cloud Testing: Fundamentals, Techniques, and Tools. Springer.
Balalaie, A., et al. (2020). Microservices Architecture: Aligning Principles, Practices, and Culture. O'Reilly Media.
Frappier, M., & Bahrami, M. (2020). Advances in Geo-Information Science. Springer.
Liu, C., & Zhou, M. (2020). Intelligent Big Geospatial Data Analytics: Deep Learning and Neural Networks. CRC Press.
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