## 3 Methods  
### Wildfire Image Classification  
A comprehensive study has been conducted to collect 715 wildfire cases between 2010 and 2020 around the globe. The sources were from the news, research papers, and fire department reports (British Columbia wildfire service website and Cal Fire website). Several incidents have complete reports, which were very helpful to generate a wildfire database. The database consists of dates, locations, a geographic coordinate system, burned areas, causes, suppression costs, and casualties. It also refers to and compares with other databases, such as Monitoring Trends in Burn Severity (MTBS), the National Interagency Fire Center (NIFC), the Global Fire Emissions Database (GFED), the Fire-CCI dataset, and the TERN AusCover dataset [35; 36; 37]. The differences and limitations were the burned area size and the designated locations. In this study, the fire sizes collected worldwide are more than 0.05 km\({}^{2}\), classified based on National Wildfire Coordinating Group (NWCG) values. The wildfire database development is the beginning of the study method, as illustrated in Figure 5.  
Satellite imagery data were then retrieved following the previously created wildfire database information. Doves (3-m resolution), Sentinel-2 (10-m resolution), Landsat-8 (30-m resolution), and MODIS (250-m resolution) data have been chosen to be downloaded through the Sentinel Hub EO browser and Planet Explorer using free registered as well as the Education and Research Standard (PlanetScope) accounts, respectively. Having multi-resolution data creates a robust dataset of the DL training afterward. Generally, a CubeSat imaging payload is within the chosen resolution, and the KITSUNE payload also falls within the selected resolution range. Moreover, previous studies have utilized multispectral bands for wildfire detection applications [28; 38; 39]. However, in this study, only the visible 3-band (RGB) spectral resolution was retrieved from satellite imagery to generate the dataset. This was due to the consistency with the spectral resolution  
Figure 5: Flowchart of the study methods. installed for the KITSUNE camera sensor and, eventually, eased the training process of the DL algorithm. A DL algorithm can be created after the satellite imagery dataset has been collected. Four labels of the dataset were manually organized: (1) wildfire, (2) land, (3) cloud, and (4) sea. Each label consisted of a thousand images. The convolution neural network (CNN) parameters are set in the training script, which runs in Colab, generating the pre-trained model in the '.hdf5' file, classification report, and confusion matrix. Several parameters have been tuned extensively: (1) input pixel, (2) data augmentation, (3) CNN models, (4) optimizers, and (5) learning rate. Then, the dataset was divided into training and testing, 75% and 25%, respectively. The labels were pre-processed to 100 \(\times\) 100 pixels (128 and 224 pixels for comparison) before training using the CNN networks. Multiple combinations of variables were tested, including type of optimizer (SGD and Adam), learning rate (0.005 and 0.05), and data augmentation (rotate, zoom, shift, and flip). Three models: (1) ShallowNet, (2) LeNet, and (3) MiniVGGNet were chosen, based on the evaluation metrics, to complement the classification results as well as the limitation of executing on the RPi CM3+ platform. The performance of a classification model depended on the number of correct and incorrect predictions. True positive (_TP_) referred to pixels assigned as the target object in both ground truth and detection results. If pixels were identified as the target object in prediction only and not in the ground truth, they are called false positives (_FP_). False negatives (_FN_) and true negatives (_TN_) are vice versa from the _FP_ and _TP_, respectively. These four parameters are essential for illustrating the confusion matrix and calculating the performance.