Leveraging CNNs and Ensemble Learning for Automated Disaster Image Classification
This addresses disaster management by improving image classification for response and recovery, but it is incremental as it combines existing methods like CNNs and ensemble learning.
The paper tackled automated classification of natural disaster images by developing a stacked CNN ensemble with XGBoost as a meta-model, achieving 95% accuracy and F1 scores up to 0.96 for individual classes.
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.