  Classification of the extent of damage suffered by a building in a seismic
event is crucial from the safety perspective and repairing work. In this study,
authors have proposed a CNN based autonomous damage detection model. Over 1200
images of different types of buildings-1000 for training and 200 for testing
classified into 4 categories according to the extent of damage suffered.
Categories are namely, no damage, minor damage, major damage, and collapse.
Trained network tested by the application of various algorithms with different
learning rates. The most optimum results were obtained on the application of
VGG16 transfer learning model with a learning rate of 1e-5 as it gave a
training accuracy of 97.85% and validation accuracy of up to 89.38%. The model
developed has real-time application in the event of an earthquake.
