Monitoring land cover changes is crucial for understanding how natural processes and human activities such as deforestation, urbanization, and agriculture reshape the environment. We introduce a publicly available dataset covering the entire United States from 2016 to 2024, integrating six spectral bands (Red, Green, Blue, NIR, SWIR1, and SWIR2) from Sentinel-2 imagery with pixel-level land cover annotations from the Dynamic World dataset. This combined resource provides a consistent, high-resolution view of the nation’s landscapes, enabling detailed analysis of both short- and long-term changes. To ease the complexities of remote sensing data handling, we supply comprehensive code for data loading, basic analysis, and visualization. We also demonstrate an example application—semantic segmentation with state-of-the-art models—to evaluate dataset quality and reveal challenges associated with minority classes. The dataset and accompanying tools facilitate research in environmental monitoring, urban planning, and climate adaptation, offering a valuable asset for understanding evolving land cover dynamics over time.

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.