A Review on Contrastive Learning Methods and Applications to Roof-Type Classification on Aerial Images

Unsupervised learning based on Contrastive Learning (CL) has attracted a lot of interest recently. This is due to excellent results on a variety of subsequent tasks (especially classification) on benchmark datasets (ImageNet, CIFAR-10, etc.) without the need of large quantities of labeled samples. This work explores the application of some of the most relevant CL techniques on a large unlabeled dataset of aerial images of building rooftops. The task that we want to solve is roof type classification using a much smaller labeled dataset. The main problem with this task is the strong dataset bias and class imbalance. This is caused by the abundance of certain types of roofs and the rarity of other types. Quantitative results show that this issue heavily affects the quality of learned representations, depending on the chosen CL technique.