Facade cracks are a common problem in the north of Sweden due to shifting temperatures creating frost in the facades which ultimately damages the facades, often in the form of cracks. To fix these cracks, workers must visually inspect the facades to find them which is a difficult and time-consuming task. This project explores the possibilities of creating an algorithm that can classify cracks on facades with the help of deep learning models. The idea is that in the future, an algorithm like this could be implemented on a drone that hoovers around buildings, filming the facade, and reporting back if there are any damages to the facade. The work in this project is exploratory and the path of convolutional neural networks has been explored, as well as the possibility to simulate training data due to the lack of real-world data. The experimental work in this project led to some interesting conclusions for further work. The relatively small amount of data used in this project points towards the possibility of using simulated data as a complement to real data, as well as the possibility of using convolutional neural networks as a means of classifying facades for crack recognition. The data and conclusions collected in this report can be used as a preparatory work for a working prototype algorithm.