## 6 Conclusions  
In this study, we discussed the design of the KITSUNE 6U CubeSat 5-m Earth observation mission. Wildfire detection using a deep-learning (DL) algorithm was also tested along with the functional test of the satellite system. The test conditions were set as close to those of the space environment as possible to thoroughly verify the camera payload. The classification algorithm was executed in the KITSUNE flight model, resulting in 98% overall accuracy and a 97% F1 score when implementing the MiniVGGNet model. The LeNet and ShallowNet networks were implemented together onboard the CubeSat to complement and compare the classification results. Moreover, the TID radiation result also showed that the radiated environment in space may affect image classification performance onboard the satellite by a single event latch-up during the test. Extra precautions and observations should be made before executing the main mission and the image classification. Ultimately, KITSUNE will be launched in 2022, and the results discussed in this paper will be compared and verified after mission success.  
Several future works and improvements have been identified to improve the DL wildfire detection accuracy. Instead of utilizing a visible spectral band, researchers could implement multispectral or hyperspectral bands for the EO CubeSat payload. The latest COTS sensor technology has evolved and been miniaturized, so it can fit into the nanosatellite class. Other DL models and techniques can be further tested for future CubeSat projects by considering the limitations of memory and power consumption of the single-board computer integrated onboard. An intelligent CubeSat constellation operation aims to improve the valuable downlink of remote sensing big data to users on Earth. Finally, this also suggests that, instead of utilizing CNN for image classification, a fusion with image segmentation is a future direction to take to improve remote sensing techniques in wildfire applications.  
Conceptualization, M.H.b.A. and N.C.O.; methodology, M.H.b.A.; software, M.H.b.A. and V.H.S.; validation, M.H.b.A., N.C.O., V.H.S. and T.O.; formal analysis, M.H.b.A.; investigation, M.H.b.A. and N.C.O.; resources, M.C.; data curation, M.H.b.A.; writing--original draft preparation, M.H.b.A.; writing--review and editing, N.C.O., V.H.S. and M.C.; visualization, M.H.b.A. and N.C.O.; supervision, M.C.; project administration, M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to express their appreciation for the valuable comments of the associate editors and anonymous reviewers. This work was supported by KITSUNE members collaborating with the Harada Seiki Co., Ltd., and Addnics Corp. in Japan. In addition, the datasets were collected from a Planet Education and Research Standard plan subscription.  
**Conflicts of Interest:** The authors declare no conflict of interest.