  Land Cover (LC) image classification has become increasingly significant in
understanding environmental changes, urban planning, and disaster management.
However, traditional LC methods are often labor-intensive and prone to human
error. This paper explores state-of-the-art deep learning models for enhanced
accuracy and efficiency in LC analysis. We compare convolutional neural
networks (CNN) against transformer-based methods, showcasing their applications
and advantages in LC studies. We used EuroSAT, a patch-based LC classification
data set based on Sentinel-2 satellite images and achieved state-of-the-art
results using current transformer models.
