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@article{178132,
        author = {J,Immanuel Kevin and Asan Nainar M},
        title = {Disaster Impact Assessment and Recovery Planning  with Alert Dispatcher Using Google Earth Engine},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {3791-3796},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178132},
        abstract = {This project deals with speed detection and the influence of natural disasters using geolocation and satellite remote data measurement data. The feature benefits from Google Earth Engine (GEE) to process high -resolution remote measurement data, including Sentinel -1 Synthetic Aperture Radar (SAR) images and country state optical imagery. Machine learning classifiers such as Random Forest (RF) are used on multi - temporal image data for injury classification and mapping affected areas. The system focuses on disasters, including floods, forest fire, earthquakes and tsunamis, and contains a gentle dispatcher module to provide initial warnings to authorities and communities. Implementation has been tested with integration in government framework for emergency response in the real scenarios. The results show support for a strategic recovery scheme based on effective initial warnings, accurate views on disaster effects and timely geographic analysis with high resolution.},
        keywords = {Disaster Detection, Geophysical Analysis, Google Earth Engine, Recovery Scheme, Remote Sensing, Warning System},
        month = {May},
        }