Color-Base Damage Feature Enhanced Support Vector Classifier for Monitoring Quake Image

In recent times, significant natural disasters have affected our city lives. This includes the occurrence of large earthquakes that have devastated the city’s infrastructure. During such times of crisis, it is important that emergency response times be as quick as possible to mitigate harm and loss. To achieve this, priority must be given to the various types of initial emergency response. Color image monitoring has the potential to prioritize responses. It uses multi-mode data resources such as openly sourced photos on social media, smartphone cameras, CCTV, and so forth. This paper presents a method to enhance the damaged color features extracted based on a pre-trained deep architecture DenseNet-201 in order to classify damage caused by several earthquakes, whose classifiers are Bayesian optimized to minimize the loss function with cross-validation. This method is applied to a case study of an earthquake image dataset. The study incorporates five target categories, namely bridge collapse, initial smoke and fire, road damage with accident risk to expand secondary loss for relevant users, tsunami damage, and non-disaster. Some advantages have been found when using color feature extraction for monitoring quake damage and further opportunities are remarked (189 words).