Automatic detection of active fires and burnt areas in forest areas using optical satellite imagery and deep learning methods

Abstract

Forest fires have important ecological, social and economic consequences causing loss of life and property. In order to prevent these consequences, it is very important to intervene in active fires in a timely manner and to determine the extent of burnt areas as soon as possible. In such studies, remote sensing methods provide great benefits in terms of speed and cost. In recent years, various methods have been developed to segment active fires and burnt areas with satellite images. Deep learning methods successfully perform segmentation processes in many areas such as disease detection in the field of health, crop type determination in the field of agriculture, land use and building detection in the field of urbanization. In this study, a method has been developed that automatically detects both active fires and burned areas that need to be re-enacted in terms of location and area size by using the same Sentinel 2 scene in a single time using deep learning methods. In particular, a new training and validation data set was created to train the U-Net+InceptionResNetV2 (CNN) model. By combining the powerful features of U-Net with InceptionResNet V2, a convolutional neural network trained over more than one million images on the ImageNet very base, we aim to examine its capabilities in burned area and active fire detection. The model applied on the test data has been shown to give successful results with an overall accuracy of 0.97 and an IoU (Intersection over union) value of 0.88 in the detection of burnt areas, and an overall accuracy of 0.99 and an IoU value of 0.82 in the detection of active fires. Finally, when the test images that were not used in the training dataset were evaluated with the trained model, it was revealed that the results were quite consistent in the detection of active fires and burnt areas and their geographical locations. © 2025 Elsevier B.V., All rights reserved.