Damage Detection on Building Façades Using Multi-Temporal Aerial Oblique Imagery

Abstract

<p><strong>Abstract.</strong> Over the past decades, a special interest has been given to remote-sensing imagery to automate the detection of damaged buildings. Given the large areas it may cover and the possibility of automation of the damage detection process, when comparing with lengthy and costly ground observations. Currently, most image-based damage detection approaches rely on Convolutional Neural Networks (CNN). These are used to determine if a given image patch shows damage or not in a binary classification approach. However, such approaches are often trained using image samples containing only debris and rubble piles. Since such approaches often aim at detecting partial or totally collapsed buildings from remote-sensing imagery. Hence, such approaches might not be applicable when the aim is to detect façade damages. This is due to the fact that façade damages also include spalling, cracks and other small signs of damage. Only a few studies focus their damage analysis on the façade and a multi-temporal approach is still missing. In this paper, a multi-temporal approach specifically designed for the image classification of façade damages is presented. To this end, three multi-temporal approaches are compared with two mono-temporal approaches. Regarding the multi-temporal approaches the objective is to understand the optimal fusion between the two imagery epochs within a CNN. The results show that the multi-temporal approaches outperform the mono-temporal ones by up to 22% in accuracy.</p>

The use of unmanned aerial vehicles (UAVs) for engineering geology applications

“…As mentioned above, the survey of the level of damages of the structures was conducted immediately after the earthquake in a traditional way, since in 2009 the UAV photogrammetry techniques were not very common. Instead, today several pieces of research are focused on the detection and assessment damages in a post-disaster scenario with these techniques (Calantropio et al 2018;Duarte et al 2019;Mavroulis et al 2019;Vetrivel et al 2018).…”

A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection

“…One of the objectives for using point clouds is the temporal comparison looking for deformations in structures in reconstructions performed in different periods. In order to compare two structures, both reconstructions must have points of interest in common [ 23 ].…”

Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data

“…However, the developed deep learning-based change detection methods aim at detecting scene-based changes rather than a specific object and need further processes to be used in RS applications. Deep learning, in particular CNN, has also been used for disaster-based applications, such as structural damage assessment [45][46][47][48][49], as well as landslide [50,51] and fire detection [52]. Most of the developed methods for building damage assessments require VHR UAV images and/or 3D point clouds and aim at only assessing structural damages.…”