Abstract
This study presents a novel approach for roof detail extraction and vectorization using remote sensing images. Unlike previous geometric-primitive-based methods that rely on the detection of corners, our method focuses on edge detection as the primary mechanism for roof reconstruction, while utilizing geometric relationships to define corners and faces. We adapt the YOLOv8 OBB model, originally designed for rotated object detection, to extract roof edges effectively. Our method demonstrates robustness against noise and occlusion, leading to precise vectorized representations of building roofs. Experiments conducted on the SGA and Melville datasets highlight the method's effectiveness. At the raster level, our model outperforms the state-of-the-art foundation segmentation model (SAM), achieving a mIoU between 0.85 and 1 for most samples and an ovIoU close to 0.97. At the vector level, evaluation using the Hausdorff distance, PolyS metric, and our raster-vector-metric demonstrates significant improvements after polygonization, with a close approximation to the reference data. The method successfully handles diverse roof structures and refines edge gaps, even on complex roof structures of new, excluded from training datasets. Our findings underscore the potential of this approach to address challenges in automatic roof structure vectorization, supporting various applications such as urban terrain reconstruction.
Abstract (translated)
这项研究提出了一种使用遥感图像提取和矢量化屋顶细节的新方法。与以往基于几何原语的方法依赖于角点检测不同，我们的方法主要侧重于边缘检测作为屋顶重建的主要机制，并利用几何关系来定义角点和面。我们对 YOLOv8 OBB 模型进行了改进，该模型最初是为旋转物体检测设计的，以有效地提取屋顶边缘。我们的方法展示了在噪声和遮挡情况下的鲁棒性，从而能够生成精确的建筑屋顶矢量表示。我们在 SGA 和 Melville 数据集上进行的实验突显了这种方法的有效性。 在栅格层面，我们的模型优于最先进的基础分割模型（SAM），大多数样本的 mIoU 在 0.85 到 1 之间，并且 ovIoU 接近 0.97。在矢量层面上，使用 Hausdorff 距离、PolyS 指标以及我们提出的栅格-矢量指标进行评估，在多边形化后表现出显著改进，接近参考数据。 该方法成功处理了各种屋顶结构，并且即使对于从未参与训练的数据集中的复杂屋顶结构也能精炼边缘缺口。我们的研究结果强调了此方法在自动屋顶结构矢量化挑战中应用的潜力，支持诸如城市地形重建等各种应用场景。
URL
https://arxiv.org/abs/2503.09187
