Preliminary Study on Land Cover Mapping in Village on Transitional Volcanic Landscape Using Deep Learning with UAV Orthophoto
Keywords:
deep learning, Land Cover Mapping, DEEPNESS, UAV orthophotoAbstract
This preliminary study explores the use of DEEPNESS, a deep learning tool via QGIS, for land cover mapping in the Tubansari Sub-Village - Bompon Watershed, a transitional volcanic landscape with complex human and environmental dynamics. The region faces challenges such as rapid land cover changes, landslides, erosion, and unsustainable land use driven by population growth and agricultural expansion. DEEPNESS efficiently processed 85.1 hectares of 10 cm resolution UAV orthophoto in about 5 seconds. However, its segmentation accuracy was unsatisfactory for village areas, mainly due to the model's training on datasets from regions like Poland, which differ in key features like building types and roof structures. Despite this, the study highlights the potential of deep learning for large-scale land cover mapping. Future work should focus on fine-tuning the model with localized data, exploring urban areas, and using higher-resolution or multi-spectral imagery to improve accuracy. This research lays the foundation for advancing land cover mapping to support sustainable land management, disaster mitigation, and environmental conservation in similar volcanic landscapes.
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