Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model

• URL: http://arxiv.org/abs/2310.16717v3
• Date: Mon, 11 Mar 2024 15:54:38 GMT
• Title: Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model
• Authors: Kai Li, Yupeng Deng, Yunlong Kong, Diyou Liu, Jingbo Chen, Yu Meng, Junxian Ma
• Abstract summary: We propose a promptable framework for roof and offset extraction. Within this framework, we propose a novel Offset-Building Model (OBM) Our model reduces offset errors by 16.6% and improves roof Intersection over Union (IoU) by 10.8% compared to other models.
• Score: 11.1278832358904
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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