Leveraging CNNs and Ensemble Learning for Automated Disaster Image Classification

TL;DR

This paper demonstrates that ensemble CNN models, especially stacked CNNs with XGBoost, can effectively classify disaster images with high accuracy, aiding disaster management efforts.

Contribution

It introduces a stacked CNN ensemble approach with hyperparameter tuning that significantly improves disaster image classification accuracy.

Findings

Achieved 95% accuracy in disaster image classification.

Stacked CNN with XGBoost outperforms individual models.

Hyperparameter tuning is crucial for optimal performance.

Abstract

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.