Early detection of wildfires has been limited using the sun-synchronous orbit satellites due to their low temporal resolution and wildfires’ fast spread in the early stage. NOAA’s geostationary weather satellites GOES-R Advanced Baseline Imager (ABI) can acquire images every 15 min at 2 km spatial resolution, and have been used for early fire detection. However, advanced processing algorithms are needed to provide timely and reliable detection of wildfires. In this research, a deep learning framework, based on Gated Recurrent Units (GRU), is proposed to detect wildfires at early stage using GOES-R dense time series data. GRU model maintains good performance on temporal modelling while keep a simple architecture, makes it suitable to efficiently process time-series data. 36 different wildfires in North and South America under the coverage of GOES-R satellites are selected to assess the effectiveness of the GRU method. The detection times based on GOES-R are compared with VIIRS active fire products at 375 m resolution in NASA’s Fire Information for Resource Management System (FIRMS). The results show that GRU-based GOES-R detections of the wildfires are earlier than that of the VIIRS active fire products in most of the study areas. Also, results from proposed method offer more precise location on the active fire at early stage than GOES-R Active Fire Product in mid-latitude and low-latitude regions.

The Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite has been used for the early detection and daily monitoring of active wildfires. How to effectively segment the active fire pixels from VIIRS image time-series in a reliable manner remains a challenge because of the low precision associated with high recall using automatic methods. For active fire detection, multi-criteria thresholding is often applied to both low-resolution and mid-resolution Earth observation images. Deep learning approaches based on Convolutional Neural Networks are also well-studied on mid-resolution images. However, ConvNet-based approaches have poor performance on low-resolution images because of the coarse spatial features. On the other hand, the high temporal resolution of VIIRS images highlights the potential of using sequential models for active fire detection. Transformer networks, a recent deep learning architecture based on self-attention, offer hope as they have shown strong performance on image segmentation and sequential modelling tasks within computer vision. In this research, we propose a Transformer-based solution to segment active fire pixels from the VIIRS time-series. The solution feeds a time-series of tokenized pixels into a Transformer network to identify active fire pixels at each timestamp and achieves a significantly higher F1-Score than prior approaches for active fires within the study areas in California, New Mexico, and Oregon in the US, and in British Columbia and Alberta in Canada, as well as in Australia and Sweden.

Wildfire monitoring and prediction are essential for understanding wildfire behaviour. With extensive Earth observation data, these tasks can be integrated and enhanced through multi-task deep learning models. We present a comprehensive multi-temporal remote sensing dataset for active fire detection, daily wildfire monitoring, and next-day wildfire prediction. Covering wildfire events in the contiguous U.S. from January 2017 to October 2021, the dataset includes 3552 surface reflectance images and auxiliary data such as weather, topography, land cover, and fuel information, totalling 71 GB. Each wildfire’s lifecycle is documented, with labels for active fires (AF) and burned areas (BA), supported by manual quality assurance of AF and BA test labels. The dataset supports three tasks: a) active fire detection, b) daily burned area mapping, and c) wildfire progression prediction. Detection tasks use pixel-wise classification of multi-spectral, multi-temporal images, while prediction tasks integrate satellite and auxiliary data to model fire dynamics. This dataset and its benchmarks provide a foundation for advancing wildfire research using deep learning.

Earth observation-based burned area mapping is critical for evaluating the impact of wildfires on ecosystems. Optical satellites, such as Landsat and Sentinel-2, are often to map burned areas. However, they suffer from interference caused by clouds. Capable of penetrating through clouds, Synthetic Aperture Radar (SAR) at C- and L-band is also widely used for burned area mapping. With a longer wavelength than C-band SAR, L-band SAR is more sensitive to trunks and branches. Conversely, C-band SAR is prone to reflection off tree canopy leaves. Thus, the wavelength differences between the two types of sensors result in varying abilities to detect burned areas with different burn severities, as different burn severities cause structural changes in the forests. This research compares ALOS Phased-Array L-band Synthetic Aperture Radar-2 (PALSAR-2) to Sentinel-1 C-band SAR for mapping burned areas across low, medium, and high burn severities. Moreover, a deep-learningbased workflow is utlized to segment burned area maps from both C-band and L-band images. ConvNet-based and Transformerbased segmentation models are trained and tested on global wildfires in broadleaf and needle-leaf forests. The results indicate that L-band data show higher backscatter changes compared to C-band data for low and medium severity. Additionally, the segmentation models with L-band data as input achieve higher F1 (0.840) and IoU Scores (0.729) than models with C-band data (0.757, 0.630). Finally, the ablation study tested different combinations of input bands and the effectiveness of totalvariation loss. The study highlights the importance of SAR Logratio images as input and demonstrates that total- variation loss can reduce the noise in SAR images and improve segmentation accuracy.