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@article{198006,
        author = {Prakash PS and Aby George Mathew and Aadil Mohammed and Khadija Sulfi and Reeja S L},
        title = {Land Use Land Cover-Based Intelligent System for Urban and Environmental Applications},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11289-11294},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198006},
        abstract = {Rapid urban growth and environmental damage have greatly increased the need for effective and easy-to-use land monitoring systems. This paper presents a web-based platform for Land Use and Land Cover (LULC) analysis that combines satellite remote sensing, geospatial data processing, and deep learning methods. The system allows users to define an Area of Interest (AOI), create LULC maps using the Dynamic World V1 dataset, and conduct multi-year comparisons along with detailed change detection. To improve analytical capabilities, a U-Net-based deep learning model is used to forecast future land changes by learning spatial patterns from historical data. The system produces probability-based heatmaps that highlight areas with a higher chance of urban growth. It also provides automated analytical insights and report generation, helping users better un-derstand complex geospatial information. The results show that the proposed system effectively identifies urban expansion and environmental changes while offering a user-friendly interface for analysis. By combining powerful computational methods with straightforward visualization, the platform aids decision-making in urban planning, environmental monitoring, and sustainable land management.},
        keywords = {Land Use Land Cover (LULC), Satellite Remote Sensing, Multi-Temporal Analysis, Deep Learning, U-Net Architecture, Pixel-Level Change Detection, Urban Growth Prediction},
        month = {April},
        }