# Disaster Recognition and Classification Based on Improved ResNet-50 Neural Network
## 1 Introduction  
With the intensification of global climate change and the acceleration of urbanization, the frequency and severity of natural disasters have significantly increased. Events such as floods, mudslides, and landslides pose substantial threats to human lives, property, and socio-economic development [1]. In response, governments and international organizations have increasingly prioritized disaster management and emergency response, promoting research into advanced disaster recognition technologies [2]. Rapid and accurate disaster classification is critical for timely emergency decision-making. Traditional disaster classification methods primarily relied on manual observation [3]. However, traditional disaster classification methods, such as manual observations from meteorologists, emergency responders, and local residents, suffer from several limitations. These approaches are often subjective, prone to delays, heavily influenced by environmental conditions, and difficult to scale during large-scale or rapidly evolving disaster events. Furthermore, manual reporting lacks the immediacy and consistency required for real-time disaster response and decision-making [4].  
With the rapid development of machine learning technologies, particularly in computer vision and pattern recognition, automated disaster recognition systems have emerged as a promising alternative. Machine learning models can extract complex features from disaster images, achieve faster and more accurate classification results, and reduce reliance on human intervention. In particular, deep learning architectures such as convolutional neural networks (CNNs) and residual networks (ResNets) enable scalable, real-time classification across diverse and complex disaster scenarios, addressing the limitations inherentin traditional methods [5]. With the development of machine learning, techniques such as support vector machines (SVMs) [6], K-nearest neighbors (KNNs) [7], and decision trees [8] have been employed. However, these approaches generally focus on single-disaster scenarios and often struggle to generalize across diverse disaster types [9; 10]. Deep learning models, particularly convolutional neural networks (CNNs), have shown promise in disaster classification. Nevertheless, conventional CNN architectures face challenges such as performance degradation, slow convergence, and the gradient vanishing problem in deeper networks [11; 12]. Emerging models like transformers [13; 14] and YOLO architectures [15; 16] have been introduced, improving disaster recognition in some contexts. However, these models typically require large datasets and intensive computational resources [17; 18], making them less practical for scenarios with limited labeled disaster data.  
Given these challenges, there is a pressing need to develop deep learning models that are both efficient and capable of robust feature extraction under data-scarce and complex disaster environments [19; 20]. In this study, we propose an improved ResNet-50-based model for multi-disaster classification, targeting seven types of natural disasters: earthquake, fire, flood, mudslide, avalanche, landslide, and land subsidence. Leveraging its residual structure and skip connections, the model mitigates the gradient vanishing problem, enhances deep feature extraction, and improves classification performance.  
The remainder of this paper is organized as follows: Section 2 describes the dataset preparation and data augmentation methods, and also outlines the model architecture and training procedure. Section 3 presents experimental results and performance comparisons. Section 4 concludes the study and discusses future research directions.