DeepAerialMapper: Deep Learning-based Semi-automatic HD Map Creation for Highly Automated Vehicles
Robert Krajewski, Huijo Kim

TL;DR
DeepAerialMapper introduces a semi-automatic deep learning approach to generate high-definition maps from aerial imagery, significantly reducing manual effort and achieving over 96% accuracy in lane and border detection.
Contribution
The paper presents a novel semi-automatic method using neural networks for HD map creation from aerial images, including a new dataset and a pipeline that outputs maps compatible with standard tools.
Findings
Achieved over 96% recall and precision in lane and border detection.
Developed a publicly available dataset of aerial images for urban roads in Germany.
Created a pipeline that produces maps in Lanelet2 format for easy extension.
Abstract
High-definition maps (HD maps) play a crucial role in the development, safety validation, and operation of highly automated vehicles. Efficiently collecting up-to-date sensor data from road segments and obtaining accurate maps from these are key challenges in HD map creation. Commonly used methods, such as dedicated measurement vehicles and crowd-sourced data from series vehicles, often face limitations in commercial viability. Although high-resolution aerial imagery offers a cost-effective or even free alternative, it requires significant manual effort and time to transform it into maps. In this paper, we introduce a semi-automatic method for creating HD maps from high-resolution aerial imagery. Our method involves training neural networks to semantically segment aerial images into classes relevant to HD maps. The resulting segmentation is then hierarchically post-processed to generate…
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Taxonomy
TopicsRobotics and Sensor-Based Localization · Advanced Image and Video Retrieval Techniques · Advanced Vision and Imaging
