The sample dataset, called the AUTH-Unreal-Wildfire (AUW) dataset, is a synthetic collection created to advance deep learning for wildfire segmentation. It addresses the critical challenge of obtaining accurately annotated training data in natural disaster management by using a novel, open-source pipeline built with the AirSim simulator. This pipeline uniquely integrates a custom particle segmentation camera and Procedural Content Generation (PCG) tools to produce photorealistic wildfire images paired with precise pixel-level segmentation masks—a feature previously difficult to achieve since fire assets are typically particle-based without a defined 3D mesh. The dataset consists of 1,500 training and 200 test images and was specifically designed to train and evaluate state-of-the-art segmentation models like PIDNet, both on its own and as a data augmentation resource to enhance performance on real-world wildfire imagery.

For a comprehensive explanation of the methodology and tools used to create this synthetic dataset, please refer to the full conference paper. This work is formally published and should be cited as follows: E. Spatharis, C. Papaioannidis, V. Mygdalis and I. Pitas, “UNREALFIRE: A synthetic dataset creation pipeline for annotated fire imagery in Unreal Engine”, IEEE International Conference on Image Processing (ICIP), Workshop on Bridging the Gap: Advanced Data Processing for Natural Disaster Management – Integrating Visual and Non-Visual Insights, Anchorage, Alaska, USA, 13-17 September, 2025. The paper is available at: https://aiia.csd.auth.gr/wp-content/uploads/2025/12/SPATHARIS_ICIP_2025.pdf and at https://zenodo.org/records/18198757 .

In order to access the AUW Dataset created/assembled by Aristotle University of Thessaloniki, please complete and sign the license agreement . Subsequently, email it to Prof. Ioannis Pitas (using “TEMA – Blaze Dataset availability” as e-mail subject) so as to rece