# Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
## 1 Introduction  
Earthquakes have a consistently high frequency of occurrence and usually lead to secondary damage such as landslide and tsunami. The building is an essential part of modern human lives, which is vulnerable to earthquakes. Damaged buildings commonly lead to the main fatalities. Therefore, it is important to monitor structural health and rapidly assess building damage after earthquakes. Remote sensing can quickly and accurately capture the surface change of the earth, and the application of remote sensing for assessing damaged buildings has shown its values for post-event emergency response and reconstruction. For instance, near real-time satellite mapping played a vital role to support the government in emergency response of post-disaster relief after the 2015 Gorkha earthquake in Nepal [1]. With the development of remote sensing technologies, the amount of data obtained after an earthquake has been gradually increasing. Feature extraction is the key to make use of remotely sensed data to automatically identify damaged buildings after earthquakes. The common methods forimage features can be roughly divided into 2 categories (the hand-crafted and learned features) when dealing with images. Hand-crafted features are typically derived using statistical functions based on expert knowledge. The commonly applied hand-crafted features are local binary pattern (LBP) [2], scale-invariant feature transform (SIFT) [3], and histogram of oriented gradients (HOG) [4] features. Hand-crafted features have achieved remarkable performance on various tasks. For deep learning, features can be automatically learned by training a deep neural network model. Learnt features are more effective than hand-crafted features in image classification and object representation [5; 6]. The learnt features from the Convolutional Neural Network (CNN) model showed better performance than six typical texture features calculated from pre- and post-event satellite data obtained before and after the 2010 Haiti earthquake [7]. In recent studies, CNNs have been applied in the field of identifying damaged buildings caused by the earthquake using remote sensing imagery and shown its potential in automatically discriminating damaged buildings [8]. CNNs were applied for collapse classification and spalling detection in concrete structures caused by earthquakes [9]. The results demonstrated that the proposed method achieved accurate and rapid identification of visual contents in a large volume of real-world imagery. A deep CNN was proposed to estimate the pre-event digital height model (DHM) from the single satellite image, and the DHM was trained by the post-event satellite imagery and light detection and ranging (LiDAR) data [10]. The collapsed buildings were successfully identified by analyzing the difference between pre- and post-event DHMs. The single-short multibit detector (SSD) [11] based on CNN pre-trained on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) classification-localization (CLS-LOC) dataset was applied to detect building damage using extremely few training samples [12]. The experiment proved that the pre-training method can effectively increase various indicators of the model. Deep transfer learning (TL) based on Visual Geometry Group (VGG)Net was applied for image-based structure damage recognition, and the results revealed the potential to use deep TL in image-based structural damage recognition [13]. While CNN features are more effective compared to conventional hand-crafted features, it is also possible to further improve the performance in damage detection by combing 3-dimensional (3D) point cloud features. The integration of CNN and 3D point cloud features derived from very high-resolution (VHR) oblique aerial images significantly improved the model transferability accuracy compared to the accuracy achieved by CNN features alone [14]. While CNNs have great potential for the task of identifying the damaged buildings, there are some problems in training a deep CNN model from scratch.