  More accurate extraction of invisible building footprints from
very-high-resolution (VHR) aerial images relies on roof segmentation and
roof-to-footprint offset extraction. Existing state-of-the-art methods based on
instance segmentation suffer from poor generalization when extended to
large-scale data production and fail to achieve low-cost human interactive
annotation. The latest prompt paradigms inspire us to design a promptable
framework for roof and offset extraction, which transforms end-to-end
algorithms into promptable methods. Within this framework, we propose a novel
Offset-Building Model (OBM). To rigorously evaluate the algorithm's
capabilities, we introduce a prompt-based evaluation method, where our model
reduces offset errors by 16.6% and improves roof Intersection over Union (IoU)
by 10.8% compared to other models. Leveraging the common patterns in predicting
offsets, we propose Distance-NMS (DNMS) algorithms, enabling the model to
further reduce offset vector loss by 6.5%. To further validate the
generalization of models, we tested them using a new dataset with over 7,000
manually annotated instance samples. Our algorithms and dataset are available
at https://anonymous.4open.science/r/OBM-B3EC.
