发表机构
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences; College of Computing, Department of Data Science, City University of Hong Kong; Aerospace Information Research Institute, Chinese Academy of Sciences; Department of Electrical and Computer Engineering, The University of Hong Kong（中国科学院大学电子电气与通信工程学院; 香港城市大学计算学院数据科学系; 中国科学院空天信息创新研究院; 香港大学电气与计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究斜视图城市遥感影像中屋顶到地面投影位移校正问题，提出将RFOV提取设为独立任务，引入数据集ObliCity，改进DragOSM为DragRoof，实验证明DragRoof性能最优，为投影位移校正奠定基础。
AI 中文摘要
斜视图城市遥感影像在建筑物屋顶和地面之间不可避免地存在几何投影位移，导致空间结构严重扭曲。现有方法要么忽略这些变形，要么在基于分割的框架中隐式处理。本文将屋顶到地面偏移向量（RFOV）提取定义为独立学习任务，使其与语义分割解耦。引入Oblique City数据集，这是首个整合高分辨率无人机影像和全球卫星数据的大规模基准。将DragOSM改进为DragRoof，通过模拟拖动屋顶到地面的连续过程学习偏移场。实验表明DragRoof性能达最优，为斜遥感影像投影位移校正奠定基础。
英文摘要
Oblique-view urban remote sensing imagery inevitably exhibits geometric projection displacements between building roofs and footprints, leading to significant distortions in spatial structure. Existing approaches either ignore these deformations or handle them implicitly within segmentation-based frameworks, where progress is dominated by general segmentation advances rather than improvements in geometric correction. In this work, we explicitly define roof-to-footprint offset vector (RFOV) extraction as an independent learning task that decouples geometric alignment from semantic segmentation. To support this task, we introduce the Oblique City dataset (ObliCity), the first large-scale benchmark that integrates high-resolution UAV imagery and globally distributed satellite data, covering diverse city morphologies and camera perspectives. Methodologically, we reformulate DragOSM into DragRoof, an ODE-based framework inspired by human annotation behavior. By simulating the continuous process of dragging roofs toward their footprints, DragRoof learns deterministic, geometry-consistent offset fields and adaptively determines convergence through an end token. Extensive experiments on ObliCity demonstrate that DragRoof achieves state-of-the-art RFOV extraction performance, requiring fewer inference steps while delivering superior directional and length accuracy. Our dataset and model establish a principled foundation for studying projection displacement correction in oblique remote sensing imagery. The source code and dataset will be avaliable at https://github.com/likaiucas/DragRoof.
Comments12 pages
