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@article{194406,
        author = {M. Srividyareddy and M. Sai Praneeth Reddy and P. Ruthika Reddy and S. Sanjana},
        title = {GeoSight: Deep Learning–Based Satellite Image Analysis for Automated Disaster and Environmental Monitoring},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4612-4617},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194406},
        abstract = {Recent advances in remote sensing and deep learning have made it possible to analyze satellite imagery more efficiently for disaster assessment and environmental monitoring. This paper presents GeoSight, a framework designed to identify disaster-affected regions and analyze environmental changes using satellite images. The system combines convolutional neural network architectures such as U-Net and DeepLabV3+ with spectral indices including the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI). These techniques enable pixel-level segmentation of satellite imagery and help detect structural damage, vegetation variation, and water bodies.
To estimate disaster impact, GeoSight compares satellite images captured before and after an event. The resulting analysis highlights affected areas and generates damage masks together with environmental statistics. By automating satellite image interpretation, the proposed system reduces the need for manual field surveys and traditional GIS workflows. Overall, GeoSight provides a practical and scalable approach for supporting disaster management and environmental monitoring using satellite-based observations.},
        keywords = {Deep Learning, Disaster Damage Detection, NDVI, NDWI, Remote Sensing, Satellite Image Analysis.},
        month = {March},
        }