A deep learning framework for disaster recognition and classification of the damaged regions

Natural disasters are rare, but when they occur, they generally cause colossal damage. Due to climate change, the number of disasters is increasing which demands enhancement in disaster response to reduce and recover the amount of devastation caused due to disasters. A rapid assessment of the situation could facilitate an improved strategy for disaster management and recovery. However, these disasters often cause infrastructural destruction which makes the affected regions inaccessible. In such difficult conditions, aerial images captured through drones can be momentous to identify the regions of damage. This study provides a methodology to classify the disaster images using deep learning, which could help to identify the regions of damage. To perform this classification, a CNN model was used which was trained on various disaster images through transfer learning. The model was trained on the AIDER dataset and provided an F1-Score of 96.8%. The performance of the proposed model is also verified with real-time videos covering the recording of various disasters. The results obtained in the experiment emphasise disaster response management and ways by which the proposed model could assist the role of deep learning to expedite rescue operations.