Semi-supervised Learning from Street-View Images and OpenStreetMap for
  Automatic Building Height Estimation
Hao Li Zhendong Yuan Gabriel Dax Gefei Kong Hongchao Fan Alexander Zipf Martin Werner
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.