Building identification is an important task for urban planning, settlement tracking, and can also help to supplement the limited data in developing countries where there is inadequate and infrequent census data. Several Deep learning architectures such as Fully connected network (FCN), UNET and Deeplab can be used to perform building identification in such scenarios where census data is limited and have given promising results. However, most of these architectures have some drawbacks such as poor edge detection thus necessitating the use of very huge training datasets that in turn leads to the utilization of a lot of computation resources. Additionally, there is a challenge when it comes to adapting these trained models to other domains, i.e., a model trained in one region poorly performs on other regions. This research aims to conduct a comparative study of the different architectures used for building identification.