Optimizing land use and land cover classification with deep learning on multi-resolution datasets

Land use and land cover (LULC) classification is essential for environmental monitoring, urban planning, and resource management. This study explores the performance of three state-of-the-art deep learning architectures, MobileNetV3, ResNet34, and GoogleNet, which were enhanced with transfer learning, data augmentation, and adaptive learning rate scheduling. We evaluate these models on two benchmark datasets: EuroSAT, consisting of Sentinel-2 satellite imagery across 10 land cover classes, and PatternNet, a high-resolution aerial dataset with 38 diverse classes. The results demonstrate that MobileNetV3 achieved the highest overall accuracy (97.83% on EuroSAT and 99.23% on PatternNet) with minimal inference time, making it ideal for real-time applications. ResNet34 achieved 97.56% and 99.06% accuracy, respectively, excelling in classifying complex, visually similar classes due to its residual learning blocks. GoogleNet's balanced performance and efficiency achieved 97.36% and 99.58% accuracy across both datasets. An ablation study confirmed that data augmentation, transfer learning, and learning rate scheduling contributed to improvements in accuracy of 5-13%. This research highlights the effectiveness of modern deep learning architectures and optimized training pipelines for LULC classification across diverse datasets, providing a foundation for future advancements in cross-domain remote sensing applications.