Leveraging Topology for Domain Adaptive Road Segmentation in Satellite
  and Aerial Imagery
Javed Iqbal Aliza Masood Waqas Sultani Mohsen Ali
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.