Blaze: A Dataset For Wildfire And Burnt Area UAV Image Classification And Segmentation

The easy and cost-effective deployment of Unmanned Aerial Vehicles (UAVs) that can fly over an area, and collect data for wildfire detection/assessment or for determining the extent of the disaster (e.g., measuring the area of burnt forest regions) using Deep Neural Network (DNN) algorithms, highlights the importance of UAVs and Artificial Intelligence (AI) algorithms for Natural Disaster Management (NDM). However, there is a limited availability of properly annotated data for the abovementioned tasks, which are typically necessary for training accurate and reliable DNN-based AI algorithms. In this direction, this paper introduces the BLAZE dataset, comprising approximately 5.4K annotated RGB images depicting both urban and non-urban areas before, during and after a wildfire. Moreover, using the proposed dataset, several baseline DNN-based algorithms have been trained and evaluated for the wildfire image classification and burnt areas segmentation tasks. Experimental results show that increased accuracy can be achieved for both tasks, thus proving the usefulness of the developed BLAZE dataset in the NDM domain. Data can be found here https://aiia.csd.auth.gr/blaze-fire-classification-segmentation-dataset/.