As climate change increases the frequency and intensities of natural disasters, it becomes more important that there are automated mechanisms in place to assess the devastation incurred and inform the timely and targeted allocation of resources and personnel for disaster relief. In this session, we welcome contributions applying artificial intelligence techniques, including various machine learning, deep learning, and computer vision approaches, to aid in this effort. For example, convolutional neural networks are trained on multi-temporal satellite imagery to harness change detection and analyze how infrastructure has been damaged. Natural language processing for social media data during these crisis events is an additional example of research at this exciting nexus. Random forest ensemble models for the prediction of the occurrence of natural disasters can be utilized to have robust disaster response systems in place. As we focus on one very important aspect of climate change mitigation, interdisciplinary contributions are especially invited.