Deep Learning Approaches for Land Use Classification in Remote Sensing Using GIS Data

  • Authors

    • Farhan Malik IT Manager, Systems Limited, Pakistan Author
    • Zara Ahmed Business Development Manager, Engro Corporation, Pakistan Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2018PII7N2R

    Published 07-05-2018

  • Deep Learning, Land Use Classification, Remote Sensing, GIS Data, Convolutional Neural Networks, Land Cover, Satellite Imagery, Data Augmentation, Model Generalization, Multisource Data Fusion

    Issue

    Vol. 1 No. 2 (2018)

    Section

    Articles

    How to Cite

    [1]
    F. Malik and Z. Ahmed, “Deep Learning Approaches for Land Use Classification in Remote Sensing Using GIS Data”, IJETMR, vol. 1, no. 2, pp. 01–06, Jul. 2018, doi: 10.67228/30715636/IJETMR-2018PII7N2R.

  • Abstract

    The integration of deep learning techniques with Geographic Information System (GIS) data has revolutionized land use classification in remote sensing. This paper explores various deep learning architectures, particularly Convolutional Neural Networks (CNNs), for classifying land use and land cover (LULC) from high-resolution satellite imagery. By leveraging GIS data, such as spatial and contextual information, the study aims to enhance classification accuracy and provide insights into urban planning, environmental monitoring, and resource management. The research also addresses challenges like data augmentation, model generalization, and the fusion of multisource data to improve classification performance.​

  • References

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