Forecasting an accurate wildfire profile is an essential tool for firefighters when planning an evacuation strategy. Therefore, we propose a WildfireNet that can predict the shape of the wildfire of the next day, when given historical wildfire profiles, elevation, and weather data. The motivation behind WildfireNet is to locate fires in a precise manner and be able to accurately predict upcoming fires. The model’s architecture was built in the inspiration of U-Net, which is a Convolutional Neural Network (CNN) commonly used in a biomedical image segmentation. Intersection over Union (IoU) and recall were calculated to measure the performance of the model. The model achieved an IoU score of 0.997 in the test set. Since the objective of the model is to predict upcoming fires, pixels that were labeled as fire but not on the previous days were extracted to calculate recall. In the test set, Wild-fireNet scored a recall of around 0.75 for fires that grew slowly. Overall, WildfireNet is a novel wildfire spread model and has the potential to be a tool to aid firefighters in their decision making.