Amazon Web Service–Google Cross-Cloud Platform for Machine Learning-Based Satellite Image Detection
Satellite image analysis is a critical component of Earth observation and satellite data analysis, providing detailed information on the effects of global events such as the COVID-19 pandemic. Cloud computing offers a flexible way to allocate resources and simplifies the management of infrastructure...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2078-2489/16/5/381 |
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| author | David Pacios Sara Ignacio-Cerrato José Luis Vázquez-Poletti Rafael Moreno-Vozmediano Nikolaos Schetakis Konstantinos Stavrakakis Alessio Di Iorio Jorge J. Gomez-Sanz Luis Vazquez |
| author_facet | David Pacios Sara Ignacio-Cerrato José Luis Vázquez-Poletti Rafael Moreno-Vozmediano Nikolaos Schetakis Konstantinos Stavrakakis Alessio Di Iorio Jorge J. Gomez-Sanz Luis Vazquez |
| author_sort | David Pacios |
| collection | DOAJ |
| description | Satellite image analysis is a critical component of Earth observation and satellite data analysis, providing detailed information on the effects of global events such as the COVID-19 pandemic. Cloud computing offers a flexible way to allocate resources and simplifies the management of infrastructure. In this study, we propose a cross-cloud system for ML-based satellite image detection, focusing on the financial and performance aspects of utilizing Amazon Web Service (AWS) Lambda and Amazon SageMaker for advanced machine learning tasks. Our system utilizes Google Apps Script (GAS) to create a web-based control panel, providing users with access to our AWS-hosted satellite detection models. Additionally, we utilize AWS to manage expenses through a strategic combination of Google Cloud and AWS, providing not only economic advantages, but also enhanced resilience. Furthermore, our approach capitalizes on the synergistic capabilities of AWS and Google Cloud to fortify our defenses against data loss and ensure operational resilience. Our goal is to demonstrate the effectiveness of a cloud environment in addressing complex and interdisciplinary challenges, particularly in the field of object analysis using spatial imagery. |
| format | Article |
| id | doaj-art-b2e323064b5d4967a38141fa6a28b86b |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-b2e323064b5d4967a38141fa6a28b86b2025-08-20T03:14:31ZengMDPI AGInformation2078-24892025-05-0116538110.3390/info16050381Amazon Web Service–Google Cross-Cloud Platform for Machine Learning-Based Satellite Image DetectionDavid Pacios0Sara Ignacio-Cerrato1José Luis Vázquez-Poletti2Rafael Moreno-Vozmediano3Nikolaos Schetakis4Konstantinos Stavrakakis5Alessio Di Iorio6Jorge J. Gomez-Sanz7Luis Vazquez8Department of Computer Architecture and Automation, Faculty of Informatics, Universidad Complutense de Madrid, Calle del Prof. José García Santesmases 9, 28040 Madrid, SpainOptics Department, Faculty of Optics and Optometry, Universidad Complutense de Madrid, Calle Arcos de Jalón 118, 28037 Madrid, SpainDepartment of Computer Architecture and Automation, Faculty of Informatics, Universidad Complutense de Madrid, Calle del Prof. José García Santesmases 9, 28040 Madrid, SpainDepartment of Computer Architecture and Automation, Faculty of Informatics, Universidad Complutense de Madrid, Calle del Prof. José García Santesmases 9, 28040 Madrid, SpainALMA Sistemi Srl, 00012 Guidonia, ItalyALMA Sistemi Srl, 00012 Guidonia, ItalyALMA Sistemi Srl, 00012 Guidonia, ItalyFaculty of Informatics, Universidad Complutense de Madrid, Calle del Prof. José García Santesmases 9, 28040 Madrid, SpainFaculty of Informatics, Universidad Complutense de Madrid, Calle del Prof. José García Santesmases 9, 28040 Madrid, SpainSatellite image analysis is a critical component of Earth observation and satellite data analysis, providing detailed information on the effects of global events such as the COVID-19 pandemic. Cloud computing offers a flexible way to allocate resources and simplifies the management of infrastructure. In this study, we propose a cross-cloud system for ML-based satellite image detection, focusing on the financial and performance aspects of utilizing Amazon Web Service (AWS) Lambda and Amazon SageMaker for advanced machine learning tasks. Our system utilizes Google Apps Script (GAS) to create a web-based control panel, providing users with access to our AWS-hosted satellite detection models. Additionally, we utilize AWS to manage expenses through a strategic combination of Google Cloud and AWS, providing not only economic advantages, but also enhanced resilience. Furthermore, our approach capitalizes on the synergistic capabilities of AWS and Google Cloud to fortify our defenses against data loss and ensure operational resilience. Our goal is to demonstrate the effectiveness of a cloud environment in addressing complex and interdisciplinary challenges, particularly in the field of object analysis using spatial imagery.https://www.mdpi.com/2078-2489/16/5/381serverless architectureGoogle Apps ScriptAWS LambdaSageMakermachine learningimage analysis |
| spellingShingle | David Pacios Sara Ignacio-Cerrato José Luis Vázquez-Poletti Rafael Moreno-Vozmediano Nikolaos Schetakis Konstantinos Stavrakakis Alessio Di Iorio Jorge J. Gomez-Sanz Luis Vazquez Amazon Web Service–Google Cross-Cloud Platform for Machine Learning-Based Satellite Image Detection Information serverless architecture Google Apps Script AWS Lambda SageMaker machine learning image analysis |
| title | Amazon Web Service–Google Cross-Cloud Platform for Machine Learning-Based Satellite Image Detection |
| title_full | Amazon Web Service–Google Cross-Cloud Platform for Machine Learning-Based Satellite Image Detection |
| title_fullStr | Amazon Web Service–Google Cross-Cloud Platform for Machine Learning-Based Satellite Image Detection |
| title_full_unstemmed | Amazon Web Service–Google Cross-Cloud Platform for Machine Learning-Based Satellite Image Detection |
| title_short | Amazon Web Service–Google Cross-Cloud Platform for Machine Learning-Based Satellite Image Detection |
| title_sort | amazon web service google cross cloud platform for machine learning based satellite image detection |
| topic | serverless architecture Google Apps Script AWS Lambda SageMaker machine learning image analysis |
| url | https://www.mdpi.com/2078-2489/16/5/381 |
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