# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 5 Conclusions  
This study has developed transfer-learning-based artificial intelligence models to assess building damages due to hurricanes in the U.S. southeast region. We developed our in-house building damage image dataset and subset it into (i) damage classification (i.e., _floods_ vs. _non-floods_) and (ii) damaged object detection including _damaged roof_, _damaged wall_, _flood damage_, and _structural damage_. We developed transfer learning workflows that take advantage of feature extraction from three advanced neural network models in computer vision (i.e., EfficientNet, ResNet, and MobileNet) and successfully retrained these models for building damage assessment. Finally, we evaluated the classification and object detection performance among the different models. Our major findings and contributions include  
* The transfer learning based flood damage classification models were developed using ResNet and MobileNet. A binary classification was carried out to detect _floods_ and _non-floods_ images. Several methods were used to evaluate the performance of the transfer learning models. The confusion matrix comparison showed both ResNet and MobileNet are able to correctly classify _floods_ and _non-floods_ with a relatively high accuracy. Specifically, the overall accuracy is about 76% using ResNet and 87% using MobileNet. Three metrics (precision, recall, and F1 score) were further calculated and compared between two models. The result obtained using MobileNet as the base model is consistently better than that using ResNet. For example, the F1 score, a harmonic mean of precision and recall, is about 0.88 using MobileNet. It is about 9% higher than the F1 score using ResNet (0.79). Overall, this study showed that hurricane flood damage to buildings can be correctly classified using artificial intelligence models developed using transfer learning techniques on the basis of advancing machine learning models in computer vision. * The transfer-learning-based damage detection models were developed using ResNet, MobileNet, and EfficientNet. Four damage types were captured in four object classes: _damaged roof_, _damaged wall_, _flood damage_, and _structural damage_. Two methods were primarily used to evaluate the performance of the transfer learning models for damage detection. The top three confidence scores and associated object class were tabulated for each model, showing that each model was capable of predicting the correct object class in the image; the MobileNet model consistently achieved the highest confidence score and proved to be the more accurate model in detecting hurricane damage. Then, the images of each type of damage were displayed with the top bounding box prediction for each model. Likewise, MobileNet consistently achieved the most accurate localizations of the detected damage in each image. Therefore, this study showed that various types of damage from hurricanes can be accurately detected using artificial intelligence models developed through transfer learning to further advance machine learning applications in computer vision. From creating our in-house damage assessment framework, we were able to show that a significant level of accuracy for damage classification can be achieved using transfer learning techniques on a pre-trained neural network. Given the relatively small and broad range of images used for the input data set, our classification model displayed a high degree of versatility that could be used during a spectrum of hurricane and other coastal hazard events. The object detection results highlight the model's ability to successfully identify damaged areas of buildings and structures from test data in a time span of seconds, which is necessary for more efficient damage assessment. Our work can be improved with further research into applying transfer learning techniques to create classification and object detection models trained on post-disaster imagery. Using these machine learning models would significantly reduce the time required for damage assessment.