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.
View on arXiv@article{li2025_2310.16717,
title={ Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model },
author={ Kai Li and Yupeng Deng and Yunlong Kong and Diyou Liu and Jingbo Chen and Yu Meng and Junxian Ma and Chenhao Wang },
journal={arXiv preprint arXiv:2310.16717},
year={ 2025 }
}