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Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation

Authors Hao Li, Zhendong Yuan, Gabriel Dax, Gefei Kong, Hongchao Fan, Alexander Zipf, Martin Werner

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Author Details

Hao Li
  • Technical University of Munich, Germany
Zhendong Yuan
  • Utrecht University, The Netherlands
Gabriel Dax
  • Technical University of Munich, Germany
Gefei Kong
  • Norwegian University of Science and Technology, Trondheim, Norway
Hongchao Fan
  • Norwegian University of Science and Technology, Trondheim, Norway
Alexander Zipf
  • GIScience Chair, Heidelberg University, Germany
Martin Werner
  • Technical University of Munich, Germany

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Hao Li, Zhendong Yuan, Gabriel Dax, Gefei Kong, Hongchao Fan, Alexander Zipf, and Martin Werner. Semi-Supervised Learning from Street-View Images and OpenStreetMap for Automatic Building Height Estimation. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 7:1-7:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.7

Abstract

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.

Subject Classification

ACM Subject Classification
  • Information systems
  • Information systems → Geographic information systems
Keywords
  • OpenStreetMap
  • Street-view Images
  • VGI
  • GeoAI
  • 3D city model
  • Facade parsing

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