The effective training of supervised deep learning models requires the labeling of extensive datasets, a process that is often costly and labor-intensive. Such models also face significant challenges with overfitting on the training data with true labels, leading to suboptimal performance on new datasets with slight variations in capturing sources or regions. This paper introduces Multi-class Unsupervised Curriculum Learning (Multi-class UCL), a novel deep learning framework. We demonstrate the effectiveness of this framework on the case study of land use and cover classification that bypasses the need for labeled data, thereby improving adaptability across different datasets. Multi-class UCL leverages pseudo-labels generated from a clustering technique to train the model and incorporates a selection process that ensures an equal representation of samples from each cluster, addressing the issue of class imbalance. The study evaluates the effectiveness of Multi-class UCL through comprehensive experiments on four diverse publicly available datasets: EuroSAT, SAT-6, RSSCN7, and UCMerced. These datasets have varying resolutions, come from different capturing sources, and encompass different geographical areas. The results demonstrate that the framework effectively learns and generalizes important features from the data, showing superior adaptability and performance across various datasets compared to traditional supervised models.