Blaze Fire Classification – Segmentation Dataset

The dataset will be used for wildfire image classification and burnt area segmentation tasks for Unmanned Aerial Vehicles. It is comprised of 5,408 frames of aerial views taken from 56 videos and 2 public datasets. From the D-Fire public dataset, 829 photographs were used; and from the Burned Area UAV public dataset 34 images were used. For the classification task, there are 5 classes (‘Burnt’, ‘Half-Burnt’, ’Non-Burnt’, ‘Fire’, ‘Smoke’). As for the segmentation task, 404 segmentation masks on a subset have been created, which assign to each pixel of the image the class ‘burnt’ or the class ‘non-burnt’. If one uses any part of these datasets in his/her work, he/she is kindly asked to cite the following paper: M. Siavrakas, C. Papaioannidis and I.Pitas, “BLAZE: A dataset for wildfire and burnt area UAV image classification and segmentation”, IEEE International Conference on Image Processing (ICIP), Anchorage, Alaska, USA, 13-17 September, 2025. Dataset Structure CSV files are provided containing the frames taken from every video, the class that has been assigned to them, the path to the respective segmentation mask along with the mask for the segmentation subset and the related links to the public videos and the 2 public datasets.More details on the dataset are available in the following papers: de Venâncio, P.V.A.B., Lisboa, A.C. & Barbosa, A.V. An automatic fire detection system based on deep convolutional neural networks for low-power, resource-constrained devices. Neural Comput & Applic 34, 15349–15368 (2022). DOI Tiago F.R. Ribeiro, Fernando Silva, José Moreira, Rogério Luís de C. Costa,Burned area semantic segmentation: A novel dataset and evaluation using convolutional networks,ISPRS Journal of Photogrammetry and Remote Sensing,Volume 202,2023,Pages 565-580,ISSN 0924-2716. DOI Details on acquiring the dataset can be found here.