fire tracking methods and categorizes them into two classes, including the traditional methods and DL-based methods. A comparison of two types of fire tracking methods can be seen in Figure 16. The summary of reviewed fire tracking publications is given in Table 18. 

Regarding satellite imagery from satellite videos, important work for fire and smoke detection has been performed by applying the advanced Himawari imager (AHI) sensor of the Japanese geostationary weather satellite Himawari-8. The AHI offers extremely high-temporal-resolution (10 min) multispectral imagery, which is suitable for real-time wildfire monitoring on a large spatial and temporal scale.

Based on the AHI system, Ref. ^[16][118] investigated the feasibility of extracting real-time information about the spatial extents of wildfires. The algorithm first identified possible hotspots using the 3.9 μm and 11.2 μm bands of Himawari-8, and then eliminated false alarms by applying certain thresholds. A similar work was proposed in Ref. ^[7][119], which integrated a threshold algorithm and a visual interpretation method to monitor the entire process of grassland fires that occurred in the China-Mongolia border regions. To further explore the information from AHI image series, Ref. ^[8][120] extended their previous work and proposed a multi-temporal method of background temperature estimation. The proposed method involved a two-step process for geostationary data: a preprocessing step to aggregate the images from the AHI and a fitting step to apply a single value decomposition process for each individual pixel. Each decomposition feature map can then be compared to the raw brightness temperature data to identify thermal anomalies and track the active fire. Results showed the proposed method detected positive thermal anomalies in up to 99% of fire cases. Recently, Ref. ^[10][122] proposed a new object-based system for tracking the progression of individual fires via visible and infrared satellite image series. The designed system can update the attributes of each fire event in California during 2012–2020, delineate the fire perimeter, and identify the active fire front shortly after satellite data acquisition.

The previous methods can overestimate the background temperature of a fire pixel and, therefore, leads to the omission of a fire event. To address this problem, Ref. ^[9][121] designed an algorithm that assimilated brightness temperatures from infrared images and the offset of the sunrise to the thermal sunrise time of a non-fire condition. The introduction of assimilation strategies improved the data analysis quality and computational cost, resulting in better fire detection and tracking results.

Instead of exploring the fire features via manually designed operators, Ref. ^[11][123] investigated DL-based remote wildfire detection and tracking framework from satellite image series. They firstly processed the streaming images to purify and examined raw image data to obtain ROI. Secondly, a 3D convolutional neural network (CNN) was applied to capture spatial and spectral patterns for more accurate and robust detection. Finally, a streaming data visualization model was completed for potential wildfire incidents. The empirical evaluations highlighted that the proposed CNN models outperformed the baselines with a 94% F1 score. To improve the fire detection accuracy, authors from ^[12][124] developed an effective approach of a CNN based Inception-v3 with transfer learning to train the satellite images and classify the datasets into the fire and non-fire images. The confusion matrix is introduced to specify the efficiency of the proposed model, and the fire occurred region is extracted based on a local binary pattern. More recently, Ref. ^[13][125] explored the potential of deep learning (DL)DL-based fire tracking by presenting a deep fully convolutional network (FCN)FCN to predict fire smoke, where satellite imagery in near-real-time by six bands images from the AHI sensor was used.

More DL-based methods contribute to fire detection instead of tracking. For example, Ref. ^[15][127] revised the general CNN models to enhance the fire detection performance in 2022. The proposed network consists of several convolution kernels with multiple sizes and dilated convolution layers with various dilation rates. Experimental results based on Landsat-8 satellite images revealed that the designed models could detect fires of varying sizes and shapes over challenging test samples, including the single fire pixels from the large fire zones. Similarly, Ref. ^[14][126] fused the optical and thermal modalities from the Landsat-8 images for a more effective fire representation. The proposed CNN model combined the residual convolution and separable convolution blocks to enable deeper features of the tracking target. A review of remote sensing-based fire detection is given in ^[17][140] in 2020, and more recent published works can be found in ^[18][19][20][141,142,143]. As detection is different from tracking and is out of theour scope, thwe focus here is on tracking only and do not provide the details on fire detection. Further studies could also be conducted to extend the DL-based fire detection to DL-based fire tracking.