Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866912240603496448 |
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| author | Li, Kai Deng, Yupeng Kong, Yunlong Liu, Diyou Chen, Jingbo Meng, Yu Ma, Junxian Wang, Chenhao |
| author_facet | Li, Kai Deng, Yupeng Kong, Yunlong Liu, Diyou Chen, Jingbo Meng, Yu Ma, Junxian Wang, Chenhao |
| contents | 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 methods based on instance segmentation suffer from poor generalization when extended to large-scale data production and fail to achieve low-cost human interaction. This prompt paradigm inspires us to design a promptable framework for roof and offset extraction, and transforms end-to-end algorithms into promptable methods. Within this framework, we propose a novel Offset-Building Model (OBM). Based on prompt prediction, we first discover a common pattern of predicting offsets and tailored Distance-NMS (DNMS) algorithms for offset optimization. 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, DNMS algorithms enable models to further reduce offset vector loss by 6.5\%. To further validate the generalization of models, we tested them using a newly proposed test set, Huizhou test set, with over 7,000 manually annotated instance samples. Our algorithms and dataset will be available at https://github.com/likaiucas/OBM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_16717 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model Li, Kai Deng, Yupeng Kong, Yunlong Liu, Diyou Chen, Jingbo Meng, Yu Ma, Junxian Wang, Chenhao Computer Vision and Pattern Recognition I.4.6; I.4.7; I.3.5; I.5.1 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 methods based on instance segmentation suffer from poor generalization when extended to large-scale data production and fail to achieve low-cost human interaction. This prompt paradigm inspires us to design a promptable framework for roof and offset extraction, and transforms end-to-end algorithms into promptable methods. Within this framework, we propose a novel Offset-Building Model (OBM). Based on prompt prediction, we first discover a common pattern of predicting offsets and tailored Distance-NMS (DNMS) algorithms for offset optimization. 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, DNMS algorithms enable models to further reduce offset vector loss by 6.5\%. To further validate the generalization of models, we tested them using a newly proposed test set, Huizhou test set, with over 7,000 manually annotated instance samples. Our algorithms and dataset will be available at https://github.com/likaiucas/OBM. |
| title | Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model |
| topic | Computer Vision and Pattern Recognition I.4.6; I.4.7; I.3.5; I.5.1 |
| url | https://arxiv.org/abs/2310.16717 |