Current disaster response strategies are based on damage assessments carried out on the ground, which can be dangerous following a ä destructive event. Damage assessments can also be performed remotely using satellite imagery, but are usually carried out through visual interpretation, which can take a lot of time. This thesis explored a way of using artificial intelligence to automate remote damage assessment. We implemented a dual-task U-Net deep learning model, trained it with the xBD dataset for assessing building damage, and applied the model to pre- and post-event very high resolution satellite imagery of the February 6, 2023 earthquakes in Turkey. The results were compared to damage maps produced using a traditional object based method by calculating the F1 scores associated with the outputs of each method and ground truth data that we compiled. The study areas were parts of the two cities Kahramanmaraş and Antakya. The deep learning model almost only correctly identified undamaged buildings, achieving F1 scores of 0.95 during training as well as 0.93 and 0.83 in the damage assessments of Kahramanmaras and Antakya, respectively. For the other damage classes, the best result was the classification of destroyed buildings, both in training and in the study areas, with a F1-score of 0.45 in training and 0.16 in Kahramanmaraş. The deep learning model performed similarly to the object based method. Although the thesis did not yield good damage maps in the areas of interest, it had many limitations, and there is still a lot of potential for deep learning models to be useful in building damage assessment.