Dwelling information is essential for humanitarian emergency response during or in the aftermath of disasters, especially in temporary settlement areas hosting forcibly displaced people. To map dwellings, the integration of very high-resolution remotely sensed imagery in computer vision models plays a key role. However, state-of-the-art deep learning models have two known downsides: (1) lack of generalization across space and time under changing scenes and object characteristics, and (2) extensive demand for annotated samples for training and validation. Both could pose a critical challenge during an emergency. To bypass this problem, this study deals with unsupervised domain adaptation for instance segmentation using a single-stage instance segmentation model, namely segmenting objects by location (SOLO). The goal is to adapt a SOLO model trained on a labelled source domain to detect dwellings in an unlabelled target domain. In this context, we study three domain adaptation techniques based on adversarial learning, domain discrepancy, and domain alignment mapping. We also propose domain similarity at different levels to understand its implication on domain adaptation. Experiments are conducted on very high-resolution satellite images obtained from four temporary settlement areas located in different countries and exhibiting various spatial characteristics. Analysis results show that in most source-target combinations unsupervised domain adaptation improves the performance by a large margin even surpassing a model trained with supervised learning. There is also an observed performance deviation among implemented strategies and different source-target dataset combinations. From the in-depth analysis of domain similarity at the image, object, and deep feature space levels, the former is more correlated with unsupervised domain adaptation performance.