# Disaster Recognition and Classification Based on Improved ResNet-50 Neural Network
## 2 Materials and Methods  
### Dataset Preparation  
In this study, an image dataset encompassing multiple types of disasters, covering seven different disaster categories--earthquakes, fires, floods, debris flows, avalanches, landslides, and ground subsidence--was collected. The dataset was compiled from various open-source databases and websites, with each disaster category containing 100 labeled images, resulting in a total of 700 images [21; 22]. To ensure efficient data organization and management, a hierarchical directory structure was adopted in this experiment. All images were categorized and stored according to their respective labels, with the training and test datasets placed in separate root directories. Each disaster category was assigned a dedicated subfolder containing all corresponding images. The validation dataset followed an identical directory structure to the training set, ensuring that the mapping between category labels and images remained consistent throughout the training and validation phases. To maintain the independence of the validation set and avoid potential data overlap, we carefully reviewed and reprocessed the dataset, selecting a broader range of disaster images that better reflect complex and diverse real-world scenarios. The final validation set contained 140 images (20 images per disaster category), and the training set contained 560 images. To further evaluate the robustness and generalization capability of the proposed model under different data split ratios, comparative experiments were conducted using three configurations: 9:1, 8:2, and 7:3 for training and validation sets. With the model architecture, training parameters, and data distribution held constant, we compared the model's performance under each split in terms of accuracy, precision, recall, F1-score, and other evaluation metrics. The experimental results demonstrate that the model achieved strong classification performance across all split settings. However, the 8:2 split provided the most stable validation accuracy and the best convergence behavior in terms of loss. While the 9:1 split yielded slightly higher training accuracy, the limited validation data led to greater fluctuations in validation results. Conversely, the 7:3 split resulted in slower convergence during training, likely due to a reduction in training data. Therefore, we adopted the 8:2 split as the primary training configuration in this study and maintained it in subsequent experiments to ensure stability and consistency in performance evaluation. To enhance the generalization ability of the model, we applied data augmentation and preprocessing to the dataset. For the training set, preprocessing techniques included random cropping, random horizontal flipping, and format conversion to increase data diversity and improve model robustness. Additionally, we performed normalization using the mean and standard deviation of ImageNet to ensure consistency with the input distribution of the pre-trained ResNet-50 model. For the test set, we applied resizing, center cropping, and tensor conversion to maintain feature consistency with the training data. Furthermore, to ensure uniformity in model input, we normalized the test set using the same standardization parameters as the training set. These preprocessing strategies help mitigate data distribution biases, enhancing the model's adaptability and stability in disaster classification tasks [23; 24]. To better illustrate the characteristics of the collected disaster images, Figure 1 presents representative feature-highlighted images from seven disaster categories, including avalanche, earthquake, fire, flood, land subsidence, landslide, and mudslide. Red boxes indicate key areas associated with the distinguishing features of each disaster type. Furthermore, to provide a clearer understanding of the overall workflow from data acquisition to disaster classification, Figure 2 depicts a schematic flow chart.