While extracting buildings from high-resolution remote sensing imagery has been widely conducted in automatic surveying and mapping, challenges remain in building extraction over complex scenes, particularly for densely rotated objects with fuzzy boundaries. This study proposes a rotation-aware building instance segmentation network (RotSegNet) that integrates a refined rotated detector to extract rotation equivariant and invariant features. A boundary refinement module is added to the segmentation network to extract fine-grained boundary features. We evaluated our method using the WHU building dataset and AFCities dataset. Our RotSegNet generated a minimum of 2.5% and 0.8% mean Average Precision (mAP) on the two datasets compared with other state-of-the-art methods, which shows the superiority of our method. Results also show that the proposed method can produce regularized buildings with high geometric accuracy.

中文翻译：

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从高分辨率遥感图像中进行旋转感知建筑实例分割

虽然从高分辨率遥感图像中提取建筑物已广泛用于自动测绘，但在复杂场景中的建筑物提取仍然存在挑战，特别是对于具有模糊边界的密集旋转对象。本研究提出了一种旋转感知构建实例分割网络（RotSegNet），该网络集成了一个精细的旋转检测器来提取旋转等变和不变特征。将边界细化模块添加到分割网络中以提取细粒度的边界特征。我们使用 WHU 建筑数据集和 AFCities 数据集评估了我们的方法。与其他最先进的方法相比，我们的 RotSegNet 在两个数据集上生成了至少 2.5% 和 0.8% 的平均精度 (mAP)，这表明了我们方法的优越性。