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@article{186047,
        author = {Manu Harsh T.S and Chinna V and Chandan A and PriyaDarshini B and RamBabu R and Niveditha V K},
        title = {Satellite imagery access and analysis in Python & Jupyter notebooks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {4088-4090},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186047},
        abstract = {Among the most common and destructive natural catastrophes, floods frequently cause significant harm to human lives, agriculture, and urban infrastructure. Effective catastrophe management and mitigation depend on quick and accurate flood detection. The Normalized Difference Water Index (NDWI), Which is calculated from satellite imagery, is used in this study's offline flood detection method. The suggested system, which is fully implemented in Python using Jupyter Notebook, enables users to submit satellite photos, process them interactively, and view areas that are dominated by water without using cloud-based tools like Google Earth Engine (GEE). In order to evaluate flooded areas, the proposed model was applied to the Bangalore region, Producing NDWI maps, Flood masks and histograms. The findings show that offline NDWI computation is a practical and effective way to monitor and analyze floods at the local level.},
        keywords = {},
        month = {November},
        }