Curriculum Learning in Earth Observation: This thesis explores the application of Curriculum Learning to the classification of satellite images, a key challenge in the field of Earth Observation. Satellite imagery provides an invaluable source of information for analyzing Earth’s surface, yet its complexity, volume, and diverse data formats make it difficult to process efficiently with conventional machine learning techniques. Deep Learning models, particularly Residual Networks (ResNet), have shown promise in image classification tasks, but often require extensive labeled datasets and high computational resources. This research analyzes how Curriculum Learning, an approach that tries to follow the human learning process by introducing training samples from simple to complex, can improve the efficiency and effectiveness of training models on satellite data.