Object detection in remotely sensed data is frequently stymied by applications in geographies that are different from that of the training data. When objects are rare, the problem is exacerbated further. This is true of assessments of energy infrastructure such as generation, transmission, and end-use consumption; key to electrification planning as well as for effective assessment of natural disaster impacts which are varying in frequency and intensity due to climate change. We propose an approach to domain adaptation that requires only unlabeled samples from the target domain and generates synthetic data to augment training data for targeted domain adaptation. This approach is shown to work consistently across four geographically diverse domains, improving object detection average precision by 15.5\% on average for small sample sizes.