Computer Science Department, Universidad de Alcalá, 28801 Alcalá de Henares, Spain.
Sensors (Basel). 2023 Feb 22;23(5):2444. doi: 10.3390/s23052444.
This paper presents the implementation of an automatic method for the reconstruction of 3D building maps. The core innovation of the proposed method is the supplementation of OpenStreetMap data with LiDAR data to reconstruct 3D urban environments automatically. The only input of the method is the area that needs to be reconstructed, defined by the enclosing points in terms of the latitude and longitude. First, area data are requested in OpenStreetMap format. However, there are certain buildings and geometries that are not fully received in OpenStreetMap files, such as information on roof types or the heights of buildings. To complete the information that is missing in the OpenStreetMap data, LiDAR data are read directly and analyzed using a convolutional neural network. The proposed approach shows that a model can be obtained with only a few samples of roof images from an urban area in Spain, and is capable of inferring roofs in other urban areas of Spain as well as other countries that were not used to train the model. The results allow us to identify a mean of 75.57% for height data and a mean of 38.81% for roof data. The finally inferred data are added to the 3D urban model, resulting in detailed and accurate 3D building maps. This work shows that the neural network is able to detect buildings that are not present in OpenStreetMap for which in LiDAR data are available. In future work, it would be interesting to compare the results of the proposed method with other approaches for generating 3D models from OSM and LiDAR data, such as point cloud segmentation or voxel-based approaches. Another area for future research could be the use of data augmentation techniques to increase the size and robustness of the training dataset.
本文提出了一种自动重建 3D 建筑地图的方法。该方法的核心创新在于利用 LiDAR 数据补充 OpenStreetMap 数据，以自动重建 3D 城市环境。该方法的唯一输入是需要重建的区域，该区域由纬度和经度定义的包围点来表示。首先，以 OpenStreetMap 格式请求区域数据。然而，OpenStreetMap 文件中存在某些建筑物和几何形状的信息没有完全接收，例如屋顶类型或建筑物高度的信息。为了补充 OpenStreetMap 数据中缺失的信息，直接读取 LiDAR 数据，并使用卷积神经网络对其进行分析。该方法表明，仅使用西班牙一个城市地区的少量屋顶图像样本，就可以获得一个模型，并且能够推断出西班牙其他城市以及其他未用于训练模型的国家的屋顶。结果表明，对于高度数据的识别率平均值为 75.57%，对于屋顶数据的识别率平均值为 38.81%。最终推断出的数据被添加到 3D 城市模型中，生成详细而准确的 3D 建筑地图。这项工作表明，神经网络能够检测到 OpenStreetMap 中不存在但 LiDAR 数据中存在的建筑物。在未来的工作中，将提出的方法的结果与其他从 OSM 和 LiDAR 数据生成 3D 模型的方法（例如点云分割或体素方法）进行比较将是很有趣的。未来研究的另一个领域可以是使用数据增强技术来增加训练数据集的规模和鲁棒性。
