A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification

The pace of Land Use/Land Cover (LULC) change has accelerated due to population growth, industrialization, and economic development. To understand analyze this transformation, it is essential examine changes in LULC meticulously. classification a fundamental complex task that plays significant role farming decision making urban planning for long-term development the earth observation system. Recent advances deep learning, transfer remote sensing technology have simplified problem. Deep learning particularly useful addressing issue insufficient training data because reduces need equally distributed data. In study, thirty-nine models were systematically evaluated alongside multiple using consistent set criteria. Our experiments will be conducted under controlled conditions provide valuable insights future research on models. Among our models, ResNet50, EfficientNetV2B0, ResNet152 top performers terms kappa accuracy scores. required three times longer time than EfficientNetV2B0 test computer, while ResNet50 took roughly twice as long. achieved an overall f1-score 0.967 set, with Highway class having lowest score Sea Lake highest.