Deep learning model ensemble for remote sensing land use classification
This study investigates deep learning approaches for automated land use classification from high-resolution remote sensing imagery, comparing Convolutional Neural Network (CNN) and Vision Transformer (ViT) architectures. Eight semantic segmentation models were evaluated on the HRSCD dataset containing 291 aerial image pairs (0.5m resolution) with four land use classes: building, agricultural, fore
