Up-to-date building locations provide a pivotal layer of information for public health initiatives and emergency responses to identify and reach a target population. Using pre-trained deep learning models to extract buildings from high-resolution imagery reduces the time and resources required but often under-perform in new locations with landscapes and building types different from that used for training the models. This study aims to develop and evaluate deep learning models for extracting buildings from high-resolution satellite imagery for a region of interest. The main objectives are to use openly available codebase, base models, and training datasets from a region to build models, to use the pre-trained regional models to extract buildings from other locations in the region, and to assess the accuracies of the models. The regional models are also fine-tuned with data from a new location to build and evaluate local models. Two regional models for Bangladesh outperformed a baseline model for low- and middle-income countries, with a 30% increase in mean accuracy (F1-scores), in three test sites. Among the regional models, a model trained with data exclusively from the region performed better (mean F1-score: 0.57) than a model built by fine-tuning the baseline model with regional training data (mean F1-score: 0.45). Local models based on the regional models also outperformed other local models. This study demonstrates the potential of pre-trained regional models in expediting building extraction, but points out the challenges associated with limited training data.