Building Height Estimation using Street-View Images, Deep-Learning, Contour Processing, and Geospatial Data

In the recent years, there has been an increasing interest in extracting data from street-view images. This includes various applications such as estimating the demographic makeup of neighborhoods to building instance classification. Building height is an important piece of information that can be used to enrich two-dimensional footprints of buildings, and enhance analysis on such footprints (e.g., economic analysis, urban planning). In this paper, a proposed algorithm (and its open-source implementation) for automatic estimation of building height from street-view images, using Convolutional Neural Networks (CNNs) and image processing techniques, is presented. The algorithm also utilizes geospatial data that can be obtained from different sources. The algorithm will ultimately be used to enrich the Open Database of Buildings (ODB), that has been published by Statistics Canada, as a part of the Linkable Open Data Environment (LODE). Some of the obtained results for building height estimation are presented in this paper. Furthermore, current and future improvements, some challenging cases and the scalability of the system are discussed.