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@article{188847,
        author = {Huzaif Ulla Khan and M E GopalaKrishna and Manjunath M and Harshith J and Mr. Suraj Kumar B. P.},
        title = {Satellite Imagery Analysis using Machine Learning and Cloud-based Geospatial Data},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {3513-3517},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188847},
        abstract = {Satellite imagery has become an important resource for applications such as environmental monitoring, agricultural assessment, urban growth analysis, and disaster management. Despite its wide usage, manual interpretation of satellite images is inefficient and often results in limited accuracy due to the large volume and complexity of geospatial data. This paper proposes a cloud-enabled machine learning framework for the automated analysis of satellite imagery using multispectral information. Satellite datasets from Sentinel-2 and Landsat-8 are accessed and preprocessed through Google Earth Engine (GEE), enabling efficient cloud-based data handling. Feature extraction and land-use classification are carried out using Random Forest algorithms and Convolutional Neural Networks (CNN). Experimental evaluation shows an overall land-use and land-cover classification accuracy of 92.4%, along with a 65% reduction in processing time achieved through cloud-based parallel computation. The proposed approach provides a scalable, efficient, and reliable solution for real-time geospatial data analysis.Index Terms-Satellite Imagery, Deep Learning, CNN, ResNet, Environmental Monitoring, Deforestation Detection, Remote Sensing.},
        keywords = {},
        month = {December},
        }