Uni-temporal Sentinel-2 imagery for wildfire detection using deep learning semantic segmentation models

Abstract

Wildfires are common disasters that have long-lasting climate effects and serious ecological, social, and economic effects due to climate change. Since Earth observation (EO) satellites were launched into space, remote sensing (RS) has become a more efficient technique that can be used in agriculture, environmental protection, geological exploration, and wildfires. The increasing number of EO satellites orbiting the earth provides huge amounts of data, such as Sentinel-2 with its Multi Spectral Instrument (MSI) sensor. Using uni-temporal Sentinel-2 imagery, we proposed a workflow based on deep learning (DL) semantic segmentation models to detect wildfires. In particular, we created a new big wildfire dataset suitable for semantic segmentation models. We tested our dataset using DL models such as U-Net, LinkNet, DeepLabV3+, U-Net++, and Attention ResU-Net. The results are analysed and compared in terms of the F1 score, the intersection over union (IoU) score, the precision and recall metrics, and the amount of training time each model possesses. The best results were achieved using U-Net with the ResNet50 encoder, with F1-score of 98.78% and IoU of 97.38%, and we developed it into a pre-trained DL Package (DLPK) model that is able to detect and monitor the wildfire from Sentinel-2 images automatically.