LAUSR

The height information of different semantic objects is crucial for urban understanding and three-dimensional (3-D) reconstruction. Fine-grained height maps provide essential details for localization, mapping, and 3-D modeling. However, single-view height estimation from remote sensing images faces challenges in handling complex pixel relations and noisy labels. This study introduces a relation-aware and edge-preserving height refinement network (RAEPHR-Net) for single-view height estimation. The proposed network incorporates a progressively relation mining and edge preservation module to generate smooth, difference, edge, direction maps, and a direction-weighted relation refinement module to refine pixel heights based on mined relations. The refined height map is used as a pseudolabel to mitigate label noise through self-supervision. Experiments on Vaihingen, Potsdam, and DFC2019 datasets demonstrate RAEPHR-Net's superior performance in accurate height estimation and semantic detail preservation compared to existing methods. The complexity and efficiency of the proposed method also outperformed with comparison methods.