Multimodal Data Fusion: Integrating Remote Sensing and ML for Environmental Monitoring

  • Authors

    • Jose Fernandez Technology Consultant, Telefónica, Spain. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2021PI1T1Z

    Published 06-04-2021

  • Multimodal Data Fusion, Remote Sensing, Machine Learning, Environmental Monitoring, Data Preprocessing, Feature Extraction, Data Consistency, Deforestation, Urbanization, Water Quality

    Issue

    Vol. 4 No. 1 (2021)

    Section

    Articles

    How to Cite

    [1]
    J. Fernandez, “Multimodal Data Fusion: Integrating Remote Sensing and ML for Environmental Monitoring”, IJETMR, vol. 4, no. 1, pp. 01–08, Jun. 2021, doi: 10.67228/30715636/IJETMR-2021PI1T1Z.

  • Abstract

    The integration of remote sensing data with machine learning (ML) techniques has revolutionized environmental monitoring by enabling efficient data analysis and interpretation. This paper presents a comprehensive framework for multimodal data fusion, combining diverse remote sensing datasets with ML algorithms to enhance environmental monitoring capabilities. We discuss the challenges associated with heterogeneous data sources and propose solutions to address data inconsistencies. The framework encompasses data preprocessing, feature extraction, fusion methodologies, and ML model development. Case studies are presented to demonstrate the effectiveness of the proposed approach in monitoring deforestation, urbanization, and water quality. The results highlight the potential of combining remote sensing and ML for accurate and timely environmental assessments.​

  • References

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