Land Cover Image Classification
It addresses the problem of labor-intensive and error-prone traditional methods for land cover analysis, which is important for environmental monitoring and urban planning, but is incremental as it applies existing models to this domain.
This paper tackled land cover image classification by comparing convolutional neural networks and transformer-based methods, achieving state-of-the-art results on the EuroSAT dataset using transformer models.
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.