# Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
## 5 Conclusions  
Deeply pretrained CNNs have shown their potential in many fields. In this study, to explore the capability of pretrained CNN models for classifying collapse and non-collapse buildings induced by the 2010 Haiti earthquake, a pretrained VGGNet model was applied and a CNN model trained from scratch was also obtained for comparison. The results demonstrated that the pretrained VGGNet model performed better than the one learned from scratch to classify collapsed and non-collapsed buildings. Dataset augmentation was used to reduce the effect of the overfitting problem induced by the limited dataset. The appropriate dataset augmentation multiple was ensured by comparing the effect of dataset augmentation for validation loss curves and consumed time. As the available dataset was far more different from ImageNet, the pretrained VGGNet model fully fine-tuned layers achieved  
Figure 6: Performance of pretrained VGGNet model for discriminating collapsed and non-collapsed buildings.  
the best results with an OA of 88.83% and a Kappa of 75.33%. These findings not only can promote the use of CNNs that have been fine-tuned but also emphasize that large training sets are important to effective training and fine-tuning of CNNs. However, it should be pointed out that the building footprints were manually prepared in the present study, which is a limitation for rapid building damage mapping. An automatic approach should be considered to extract building footprints from high-resolution imagery. It is also possible to directly use an existing building database if available.  
The advantages of using CNNs to detect damaged buildings include feature learning without feature engineering, the ability to extract invariant features, often with high accuracy. CNN technology based on remote sensing images is still in the early stages of detecting damaged buildings since the image features are complex and limited training dataset after earthquakes. There are some other factors that affect the accuracy of classification. For instance, the buildings on remote sensing imagery might be obscured by other objects such as trees, and there might be no visible distortion of collapsed buildings with metal roofs. Therefore, some additional datasets could be applied to improve the classification accuracy if it is available, such as airborne oblique dataset which can reveal cracks of a building facade and LiDAR data which can acquire failure geometrics of earthquake-affected buildings. In addition, it is still interesting to explore how to combine CNNs with conventional methods to take full advantage of existing techniques in order to improve the quality of derived building-damage information from remotely sensed imagery.  
[PERSON] conceived, designed, and performed the research and wrote the manuscript. [PERSON] made contributions to the analysis of the data. [PERSON], [PERSON], [PERSON], and [PERSON] discussed the basic structure of the manuscript. [PERSON] reviewed the manuscript and supervised the study at all stages. [PERSON], [PERSON], [PERSON], and [PERSON] read and approved the submitted manuscript. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the Open Access Publication Funds of TU Dresden. [PERSON] was funded by National Natural Science Foundation of China (grant number 41901401) and Natural Science Foundation of Jiangsu Provincial (grant number BK20190743).  
The first author wants to express her acknowledgments to the China Scholarship Council (CSC) for providing financial support to study at TU Dresden. Open Access Funding by the Publication Fund of the TU Dresden. The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNNs & Convolutional Neural Networks \\ DHM & Digital Height Model \\ EMS & European Macroseismic Scale \\ FCL & Fully-Connected Layer \\ OA & Overall Accuracy \\ PA & Producer Accuracy \\ UA & User Accuracy \\ \end{tabular}