Getting precise aspects of road through segmentation from remote sensing imagery is useful for many real-world applications such as autonomous vehicles, urban development and planning, and achieving sustainable development goals. Roads are only a small part of the image, and their appearance, type, width, elevation, directions, etc. exhibit large variations across geographical areas. Furthermore, due to differences in urbanization styles, planning, and the natural environments; regions along the roads vary significantly. Due to these variations among the train and test domains, the road segmentation algorithms fail to generalize to new geographical locations. Unlike the generic domain alignment scenarios, road segmentation has no scene structure, and generic domain adaptation methods are unable to enforce topological properties like continuity, connectivity, smoothness, etc., thus resulting in degraded domain alignment. In this work, we propose a topology-aware unsupervised domain adaptation approach for road segmentation in remote sensing imagery. Specifically, we predict road skeleton, an auxiliary task to impose the topological constraints. To enforce consistent predictions of road and skeleton, especially in the unlabeled target domain, the conformity loss is defined across the skeleton prediction head and the road-segmentation head. Furthermore, for self-training, we filter out the noisy pseudo-labels by using a connectivity-based pseudo-labels refinement strategy, on both road and skeleton segmentation heads, thus avoiding holes and discontinuities. Extensive experiments on the benchmark datasets show the effectiveness of the proposed approach compared to existing state-of-the-art methods. Specifically, for SpaceNet to DeepGlobe adaptation, the proposed approach outperforms the competing methods by a minimum margin of 6.6%, 6.7%, and 9.8% in IoU, F1-score, and APLS, respectively.

翻译：通过从遥感图像中分割道路以获取精确的道路信息，对于自动驾驶、城市发展与规划以及实现可持续发展目标等诸多实际应用具有重要意义。道路仅占图像的一小部分，且其外观、类型、宽度、高程、方向等特征在不同地理区域间存在巨大差异。此外，由于城市化风格、规划及自然环境的差异，道路沿线区域也变化显著。由于训练域与测试域之间的这些差异，道路分割算法难以泛化到新的地理位置。与通用的域对齐场景不同，道路分割缺乏场景结构，通用的域自适应方法无法强制执行连续性、连通性、平滑性等拓扑属性，从而导致域对齐效果下降。本文提出了一种面向遥感图像道路分割的拓扑感知无监督域自适应方法。具体而言，我们预测道路骨架作为辅助任务以施加拓扑约束。为了确保在无标签目标域中道路与骨架的预测一致，我们在骨架预测头和道路分割头之间定义了一致性损失。此外，在自训练过程中，我们通过基于连通性的伪标签细化策略，在道路和骨架分割头上滤除噪声伪标签，从而避免空洞和不连续。在基准数据集上的大量实验表明，与现有最先进方法相比，本文方法具有有效性。具体而言，在SpaceNet到DeepGlobe的域自适应任务中，本文方法在IoU、F1分数和APLS指标上分别超越对比方法至少6.6%、6.7%和9.8%。