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This study investigates spatio-temporal land use and land cover (LULC) changes in Pune using Landsat 8 imagery and advanced machine learning techniques. Google Earth Engine was employed to classify imagery from 2015 to 2023 into six LULC categories, with a SMOTE-enhanced Support Vector Machine (SVM) model addressing class imbalance and achieving high classification accuracy. Outputs were analyzed in QGIS to detect urban expansion trends, revealing substantial growth in built-up and road areas, coupled with notable declines in agricultural and forest zones. A regression-based forecasting model was developed in Python to predict LULC patterns for 2026. Results indicate continued urban intensification and ecological contraction. The study provides a scalable geospatial framework for sustainable urban land management and informs evidence-based planning and policy formulation.

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Spatio-Temporal Land Use and Land Cover Analysis and Urban Expansion Prediction Using Remote Sensing and SMOTE-SVM Classification

  • 会议论文

  • 发表日期：

    2026年

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  • Priya Surana,
  • Pramod Patil,
  • Baravkar Shruti

摘要

This study investigates spatio-temporal land use and land cover (LULC) changes in Pune using Landsat 8 imagery and advanced machine learning techniques. Google Earth Engine was employed to classify imagery from 2015 to 2023 into six LULC categories, with a SMOTE-enhanced Support Vector Machine (SVM) model addressing class imbalance and achieving high classification accuracy. Outputs were analyzed in QGIS to detect urban expansion trends, revealing substantial growth in built-up and road areas, coupled with notable declines in agricultural and forest zones. A regression-based forecasting model was developed in Python to predict LULC patterns for 2026. Results indicate continued urban intensification and ecological contraction. The study provides a scalable geospatial framework for sustainable urban land management and informs evidence-based planning and policy formulation.