Shape Based Classification of Seismic Building Structural Types

Abstract

<p><strong>Abstract.</strong> This paper investigates automatic prediction of seismic building structural types described by the Global Earthquake Model (GEM) taxonomy, by combining remote sensing, cadastral and inspection data in a supervised machine learning approach. Our focus lies on the extraction of detailed geometric information from a point cloud gained by aerial laser scanning. To describe the geometric shape of a building we apply Shape-DNA, a spectral shape descriptor based on the eigenvalues of the Laplace-Beltrami operator. In a first experiment on synthetically generated building stock we succeed in predicting the roof type of different buildings with accuracies above 80<span class="thinspace"></span>%, only relying on the Shape-DNA. The roof type of a building thereby serves as an example of a relevant feature for predicting GEM attributes, which cannot easily be identified and described by using traditional methods for shape analysis of buildings. Further research is necessary in order to explore the usability of Shape-DNA on real building data. In a second experiment we use real-world data of buildings located in the Groningen region in the Netherlands. Here we can automatically predict six GEM attributes, such as the type of lateral load resisting system, with accuracies above 75<span class="thinspace"></span>% only by taking a buildings footprint area and year of construction into account.</p>

Deep Multimodal Fusion Model for Building Structural Type Recognition Using Multisource Remote Sensing Images and Building-Related Knowledge

“…However, since the BST is a description of the internal structure of a building, it is challenging to infer BSTs directly based on remote sensing images alone, which hinders the largescale application of these methods. Given a particular coupling law between BRK and building structure, other methods [1], [18], [10], [19] focus on combining BRK and remote sensing images to improve the reliability of BSTs recognition. These methods usually obtain BRK such as tax assessor and cadastral data [1], [18] from related government agencies and mapping software [10], [19].…”

“…Given a particular coupling law between BRK and building structure, other methods [1], [18], [10], [19] focus on combining BRK and remote sensing images to improve the reliability of BSTs recognition. These methods usually obtain BRK such as tax assessor and cadastral data [1], [18] from related government agencies and mapping software [10], [19]. They design handcraft features based on this knowledge and fuse them with the handcraft features of remote sensing images.…”