This Master's Thesis presents a visual method based on machine learning to estimate, from a picture of the Earth, the attitude of a satellite at the moment it captured that image. Attitude estimation is a crucial task in satellite operations as it determines the orientation and position of the satellite with respect to its surroundings. Conventional methods for estimating satellite attitude require multiple sensors and complex algorithms, making them prone to errors and limitations, especially in the case of small and low-cost satellites as CubeSats. In this work, a machine learning-based approach is presented, to be deployed on-ground, which leverages image data collected by cameras onboard the satellite to geographically localize the landmarks captured, and provide an estimation of the spacecraft attitude.