Though hurricanes are some of the most destructive natural disasters, with the frequency of intense and damaging hurricanes increasing due to climate change, current estimates of damages from hurricanes are a source of uncertainty. To better estimate home damage from hurricanes, this study constructs a novel dataset of building damages from hurricane wind and water using satellite-based observations of regions impacted by hurricanes. With imagery provided by Maxar’s Open Data Program, damages inflicted on houses were evaluated by classifying buildings before and after a hurricane and analyzing the differences. The xView2 Challenge dataset of satellite and aerial imagery, labels, and targets was used to train a convolutional neural network (CNN) to predict damages incurred by houses after a hurricane. The processed results reveal successful building localization and damage prediction relative to the targets in the training set and to out-of-sample prediction. They further reveal the importance of imagery color normalization and training on various terrain and damage types. The model achieved an overall score of 0.699, taking into account a combined dice loss coefficient of 0.847 and F1 score of 0.636. Continued work involves connecting building damage scores to monetary damage and developing wide-scale testing through model refining and experimentation on additional hurricanes captured by Maxar and other satellite image sources. This data-intensive interdisciplinary research effort will make significant advancements to improve our understanding of costs and damages of hurricanes, benefitting researchers and allowing policymakers to provide more effective pre-disaster risk management programs and post-disaster recovery assistance.