3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This paper proposes and implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. The paper applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5 metre accuracy, which is the benchmark recommended by the CityGML standard.

3D 地图对无人机导航、紧急服务和城市规划等许多应用越来越有用。但是，使用现有技术（例如激光扫描仪）创建 3D 地图并使其保持最新状态的成本很高。本文提出并实施了一种使用全球导航卫星系统 (GNSS) 信号免费生成 2.5D（也称为 3D 细节层次 (LOD) 1）地图的新方法，这些信号在全球范围内可用且仅被障碍物阻挡在卫星和接收器之间。这使我们能够找到 GNSS 信号可用性的模式并创建 3D 地图。该论文基于自举技术将算法应用于 GNSS 信号强度模式，该技术在生成地图的同时迭代训练信号分类器。所提出技术的结果证明了使用自动处理的 GNSS 数据创建 3D 地图的能力。结果表明，第三个维度，即建筑物的高度，可以以低于 5 米的精度进行估计，这是 CityGML 标准推荐的基准。