Accurate building height estimation is key to the automatic derivation of 3D city models from emerging big geospatial data, including Volunteered Geographical Information (VGI). However, an automatic solution for large-scale building height estimation based on low-cost VGI data is currently missing. The fast development of VGI data platforms, especially OpenStreetMap (OSM) and crowdsourced street-view images (SVI), offers a stimulating opportunity to fill this research gap. In this work, we propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OSM data to generate low-cost and open-source 3D city modeling in LoD1. The proposed method consists of three parts: first, we propose an SSL schema with the option of setting a different ratio of "pseudo label" during the supervised regression; second, we extract multi-level morphometric features from OSM data (i.e., buildings and streets) for the purposed of inferring building height; last, we design a building floor estimation workflow with a pre-trained facade object detection network to generate "pseudo label" from SVI and assign it to the corresponding OSM building footprint. In a case study, we validate the proposed SSL method in the city of Heidelberg, Germany and evaluate the model performance against the reference data of building heights. Based on three different regression models, namely Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the SSL method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters, which is competitive to state-of-the-art approaches. The preliminary result is promising and motivates our future work in scaling up the proposed method based on low-cost VGI data, with possibilities in even regions and areas with diverse data quality and availability.

翻译：准确的建筑高度估算是从新兴地理大数据（包括自愿地理信息，VGI）中自动生成三维城市模型的关键。然而，目前缺乏基于低成本VGI数据进行大规模建筑高度估算的自动化解决方案。VGI数据平台（尤其是OpenStreetMap，OSM）的快速发展，以及众包街景图像（SVI）的涌现，为填补这一研究空白提供了激动人心的机遇。本文提出一种基于半监督学习的自动建筑高度估算方法，利用Mapillary SVI与OSM数据，实现低成本、开源的LoD1级三维城市建模。该方法包含三个部分：首先，提出一种半监督学习框架，可在监督回归过程中灵活设置不同比例的“伪标签”；其次，从OSM数据（即建筑物与街道）中提取多层级形态特征，用于推断建筑高度；最后，设计一套建筑楼层估计流程，通过预训练的立面目标检测网络从SVI中生成“伪标签”，并将其赋予对应的OSM建筑轮廓。在案例研究中，我们于德国海德堡市验证了所提出的半监督学习方法，并基于建筑高度参考数据评估模型性能。基于随机森林（RF）、支持向量机（SVM）和卷积神经网络（CNN）三种不同回归模型，该半监督学习方法显著提升了建筑高度估算性能，平均绝对误差（MAE）约为2.1米，与当前最先进方法具有竞争力。初步结果令人鼓舞，为未来基于低成本VGI数据规模化应用该方法提供了动力，并有望推广至数据质量与可用性各异的区域。