Implementation of a Deep Learning Approach and Remote Sensing for Classification of Land Use and Land Cover in Bali

Land use and land cover (LULC) classification is essential for sustainable land use planning and natural resource management. This study develops a deep learning-based U-Net model for LULC classification in Bali Province using Landsat 8 and 9 satellite imagery, along with various geospatial variables. There are nine categories of land cover in this classification: dryland forest, mangrove forest, plantation forest, bare land, savanna and grassland, water body, dry agriculture, paddy field, and build-up. The model was trained on data from 2014, 2019, and 2024, utilizing six driver variables. The evaluation findings demonstrated excellent performance, with an overall accuracy of 0.88, an average Intersection over Union (IoU) of 0.76, and an average Dice Score of 0.86. This U-Net model can assist the Regional Development Planning Agency (Bappeda) in producing reliable base maps to support future spatial planning (RTRW), and can also be used for effective LULC monitoring.