Disaster Imagery

Disaster imagery analysis leverages computer vision, primarily deep learning techniques like convolutional neural networks (CNNs), to automate damage assessment from aerial and satellite images following natural disasters or conflicts. Current research focuses on improving model accuracy and interpretability, particularly for high-resolution imagery and diverse disaster types, often employing techniques like class activation mapping to understand model predictions. This work is crucial for expediting post-disaster response efforts by enabling rapid damage assessment and resource allocation, improving situational awareness, and supporting humanitarian aid.