AerialHDMappper: High-Definition Map Construction from Aerial Imagery

Lane-level High-Definition Map (HDMap) automatic construction from aerial imagery can significantly enhance the efficiency of HDMap production. However, some problems limit the application of aerial imagery in HDMap construction. First, the bird’s eye view of aerial imagery often causes occlusion, which leads to interrupted map element predictions. In addition, existing methods for aerial imagery-based HDMap construction can only construct road-level map and the post-processing is complicated. To solve these problems, we propose AerialHDMapper, an end-to-end framework that predicts lane-level vectorized map elements directly requiring no post-processing. The framework improves prediction accuracy in occluded areas through the introduction of the Directional Attention (DirAttn) module. AerialHDMapper is mainly composed of two parts: the image encoder and the polyline generator. The image encoder integrates DirAttn modules, which leverage information along and perpendicular to the direction of a map element, thereby improving the representation of the current position. The polyline generator employs a modified transformer architecture to identify long-sequence irregular map elements. Experimental results demonstrate that AerialHDMapper outperforms existing methods on both the CARLA Simulator Dataset and the Aerial Argoverse 2 Dataset we collected. Extensive ablation experiments further reveal that our approach exhibits strong robustness and generalization capabilities.