Natural disasters act as a serious threat globally, requiring effective and efficient disaster management and recovery. This paper focuses on classifying natural disaster images using Convolutional Neural Networks (CNNs). Multiple CNN architectures were built and trained on a dataset containing images of earthquakes, floods, wildfires, and volcanoes. A stacked CNN ensemble approach proved to be the most effective, achieving 95% accuracy and an F1 score going up to 0.96 for individual classes. Tuning hyperparameters of individual models for optimization was critical to maximize the models' performance. The stacking of CNNs with XGBoost acting as the meta-model utilizes the strengths of the CNN and ResNet models to improve the overall accuracy of the classification. Results obtained from the models illustrated the potency of CNN-based models for automated disaster image classification. This lays the foundation for expanding these techniques to build robust systems for disaster response, damage assessment, and recovery management.

翻译：自然灾害在全球范围内构成严重威胁，亟需高效且有效的灾害管理与恢复措施。本文聚焦于利用卷积神经网络（CNN）对自然灾害图像进行分类。研究构建并训练了多种CNN架构，数据集包含地震、洪水、野火及火山喷发等灾害图像。实验证明，堆叠式CNN集成方法效果最佳，整体准确率达95%，各类别F1分数最高达0.96。为最大化模型性能，对单个模型进行超参数调优至关重要。采用XGBoost作为元模型对CNN进行堆叠，充分利用CNN与ResNet模型优势，从而提升整体分类精度。模型结果充分展示了基于CNN的模型在自动化灾害图像分类中的效力，为拓展相关技术以构建稳健的灾害响应、损失评估及恢复管理系统奠定基础。