## 4 Resilience-Oriented Emergency Responses Driven by Machine Learning
### Rapid Damage Assessment  
The effectiveness and timeliness of emergency responses following damaging earthquakes critically depend on an accurate assessment of earthquake-induced damages. Traditionally, post-shock damage assessments rely on manual inspections, which are often time-consuming and struggle to inform agile decision-making; moreover, these manual inspections yield inherently subjective outcomes, potentially misleading decision-makers [92]. To address these challenges, recent research efforts have capitalized on the latest advancements in deep learning (DL) techniques to rapidly identify earthquake-induced damages. DL leverages computational models, i.e., artificial neural networks with multiple layers capable of handling complex learning tasks by processing large datasets. These models employ hierarchical representations with multiple levels of abstraction and are trained using gradient-driven optimizers in conjunction with the backpropagation algorithm [84]. A pivotal moment in DL for computer vision was the development of convolutional neural networks (CNNs), exemplified by the influential AlexNet. AlexNet's victory in the 2012 ImageNet Large Scale Visual Recognition Challenge convincingly surpassed classical approaches [93]. Subsequent advancements in hardware, software, and algorithms (such as GPUs and DL frameworks like TensorFlow 2.17.0 and PyTorch 2.4.0) have solidifiedCNNs as powerful tools for image classification [94], object detection [95; 96], and semantic segmentation [97; 98; 99]. The architecture of CNNs encodes certain prior assumptions that are usually met in many computer vision problems. Because of such inductive biases, CNNs are especially suitable for problems from this domain. Beyond the deep model's hierarchical representation, which includes both low-level and high-level features, CNNs also exhibit a certain translational invariance in the learned features. A simple CNN architecture for binary damage classification is depicted in Figure 3. It illustrates a model that classifies input images based on whether they include damaged or undamaged objects. Denoted as \(NN_{\theta}(x)\), this model has some trainable weights \(\theta\) and yields the probability \(p_{\theta}(damage\,|\,x)\) that a given input image \(x\) contains a damaged object. The complementary probability, \(p_{\theta}(no\)\(damage\,|\,x)\), is straightforwardly calculated as \(1-p_{\theta}(damage\,|\,x)\). The map from the input image to the probabilistic output usually consists of a convolutional feature extractor and a fully connected classifier. The feature extractor contains convolutional and downsampling layers, typically several. Moreover, nonlinear activation functions such as the rectified linear unit (_ReLU_) are applied to the feature maps, usually directly after the convolution. The flattened features are then further processed in the final classifier. It contains dense linear layers, each followed by an activation function. A sigmoid function is applied to the single output of the last layer so as to ensure that the predicted damage probability is properly normalized. In the context of emergency responses, researchers have already started to leverage the advances in CNNs to develop adaptive models for identifying damage in structures and CISs following destructive earthquakes. Specifically, the CNN-based image classification, object detection, and semantic segmentation for damage identification and localization have undergone comprehensive scrutiny by [PERSON] et al. [100] and [PERSON] et al. [92], respectively. Notably, [PERSON] et al. [101] employed region-based object detection methods to localize earthquake-induced damage in building structures and distinguish between various damage types. To facilitate autonomous, robot-led damage inspections, pretrained models were fine-tuned using manually annotated data collected through in situ observations of earthquakes over the past two decades.