## 1 Introduction
These satellites have the ability to monitor multiple regions around the world simultaneously, while their performance depends on spatial and temporal resolution as well. The importance of wildfire detection by satellites could be explained by providing imagery data for early-stage fires and mapping fire occurrences in order to inform regional fire departments. Currently, imagery from active EO satellites like GOES, Landsat-8, Himawari-8, and Sentinel-2 has been used for fire monitoring [28]. [PERSON] et al. [29] utilized the Sentinel-2 data to map the fire severity in southeastern Australia, while [PERSON] et al. [30] used both Sentinel-2 and aerial LiDAR data for forest fire assessment in Galacia, Spain. Most researchers have leveraged the high spatial resolution with a multi-spectral sensor by implementing artificial intelligence (AI) to detect such disasters. The high computing processing tools have created a big challenge for the satellite developers to implement processing onboard in space. The traditional EO satellites, such as GOES, MODIS, Meteosat, and Himawari-8, utilize a ground-based fire thermal anomaly (FTA) algorithm [31]. The complex algorithm adaptation has improved active fire pixel detection by approximately 6% for GOES -16 mission compared to GOES-13. On the other hand, a smaller bus system like IPEX, which is a 1U CubeSat, is limited in onboard execution of machine learning (ML), and successful mission results presented the feasibility of flying a Support Vector Machine (SVM) onboard with limited power generation [32]. In addition, the PhiSat-1 constellation of 6U CubeSats was launched in 2020, and they are currently operating in orbit. This mission includes the AI cloud detection experiment, which runs on an embedded vision processing unit (VPU) onboard the satellite [33]. However, such studies are considerably limited for other remote sensing applications by applying traditional feature extraction and ML approaches. Despite the various deep-learning (DL) techniques for satellite image classification, CubeSat platforms have not been employed particularly for onboard processing for active fire detection. This study is unique and worth publishing since it is a DL implementation onboard a small satellite platform such as a CubeSat, which has significantly limited resources compared to ground applications and traditional large-scale satellites. These limited resources could be listed as processing power, memory size, downlink capacity from the satellite to the ground station, and allocated battery power for long-duration tasks to perform DL techniques with a single-board computer (SBC). The DL model in this study could be trained on the ground with a dataset, and the resulting pre-trained model could be employed onboard CubeSat for image classification such as wildfire detection applications. Since the objective is selected as wildfire detection, the dataset has been prepared from a set of satellite images containing wildfire occurrences around the world. In addition, the small satellite in this study is a 6U CubeSat called KITSUNE, which has been recently deployed from the International Space Station (ISS). Even though the limited resources would not allow for executing the DL onboard, the pre-trained algorithm is planned to be demonstrated as a secondary mission. The on-orbit results for wildfire classification will be compared to the results on the ground after downloading the images from the camera payload. However, on-orbit DL training performance results are not in the scope of this study. In this paper, the development of the DL algorithm used for wildfire classification is discussed in addition to the integration into a small satellite system. Therefore, the camera payload, which is based on commercial off-the-shelf (COTS) components, is described together with the satellite bus system and the testing results for the space environment and functionality since they are related to the requirements to perform wildfire classification.