提示驱动的航空图像建筑物足迹提取与偏移建筑物模型
Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model
AI总结：
提出可提示的偏移建筑物模型（OBM），结合距离NMS优化，在航空图像中提升建筑物足迹提取精度，显著降低偏移误差并提高屋顶IoU。
摘要：
从超高分辨率（VHR）航空图像中更准确地提取不可见的建筑物足迹，依赖于屋顶分割和屋顶到足迹的偏移提取。现有的基于实例分割的方法在扩展到大规模数据生产时泛化能力较差，并且无法实现低成本的人工交互。这种提示范式启发我们设计了一个用于屋顶和偏移提取的可提示框架，并将端到端算法转变为可提示方法。在此框架内，我们提出了一种新颖的偏移建筑物模型（OBM）。基于提示预测，我们首先发现了预测偏移的常见模式，并针对偏移优化定制了距离非极大值抑制（DNMS）算法。为了严格评估算法的能力，我们引入了一种基于提示的评估方法，与其他模型相比，我们的模型将偏移误差降低了16.6%，并将屋顶交并比（IoU）提高了10.8%。利用预测偏移中的常见模式，DNMS算法使模型能够进一步将偏移向量损失降低6.5%。为了进一步验证模型的泛化能力，我们使用新提出的测试集——惠州测试集进行了测试，该测试集包含超过7,000个手动标注的实例样本。我们的算法和数据集将在https://github.com/likaiucas/OBM上提供。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
