Improving classification of damaged buildings post hurricane using satellite imagery

There is a growing need for efficient and accurate methods for assessing building damage, especially in post-disaster scenarios. Traditional manual inspection is time-consuming and prone to human error, highlighting the need for automated systems. Leveraging advanced deep learning models can improve the accuracy and speed of image classification, contributing to timely disaster response. This study focuses on developing an advanced deep learning model for building damage classification using image data. The proposed model leverages a hybrid architecture combining ResNet50 for transfer learning with a custom Convolutional Neural Network (CNN) to capture both global and local features effectively. The dataset used includes labeled images of buildings under different conditions, providing a diverse set for training and evaluation. The evaluation results showed an accuracy of 98.9% on the balanced dataset and 98.01% on the unbalanced dataset. The proposed model outperformed various models and demonstrated robustness across different data distributions. The study provides insights into the efficacy of hybrid models combining transfer learning and custom-designed CNNs for image-based classification tasks.