| Title: |
DEMONSTRATION OF WILDFIRE DETECTION USING IMAGE CLASSIFICATION ONBOARD CUBESAT ; Demonstration of Wildfire Detection Using Image Classification Onboard Cubesat |
| Authors: |
Azami, Muhammad Hasif bin; オルガス, ネチュミ ジハン; 30627; Orger, Necmi Cihan; 55853554700; orcid:0000-0002-5183-4545; 100001428; Schulz, Victor Hugo; 35622; シュルツ, ビクトル ユーゴ; 57188968120; 100001782; KITSUNE; 趙, 孟佑; 754; Cho, Mengu; 60243333; 7401727758; 168 |
| Publisher Information: |
IEEE |
| Publication Year: |
2021 |
| Collection: |
Kyushu Institute of Technology Academic Repository (Kyutacar) / 九州工業大学学術機関リポジトリ |
| Subject Terms: |
CubeSat; deep learning; wildfire; single-board computer; state-of-art |
| Description: |
In the past decade, a massive number of wildfire events have been reported worldwide. An economic loss every year shows the importance of having satellites to overcome this catastrophe. Earth observation CubeSat can be a solution to detect, monitor, and provide data for the fire departments from the aerial view. For this purpose, the utilization of the deep learning (DL) algorithm to process the images captured onboard CubeSat before they are downlinked to the ground station is demonstrated. As a proof-of-concept, a single-board computer (SBC), Raspberry Pi, has been integrated into the KITSUNE 6U CubeSat to run the model. In this paper, the results of functional and environment tests of our proof-of-concept implementation have shown the feasibility of using image classification onboard the CubeSat. The DL algorithm's accuracy has reached 95% in the ground test, and this accuracy can be improved with further verifications of the platform and analysis of flight results. Therefore, DL's advancement in CubeSat is the state-of-art that can contribute significantly to address the wildfire issues. ; journal article |
| Document Type: |
other/unknown material |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS; 5413; 5416; https://kyutech.repo.nii.ac.jp/record/2002035/files/10464054.pdf; https://hdl.handle.net/10228/0002002035; https://kyutech.repo.nii.ac.jp/records/2002035 |
| Availability: |
https://kyutech.repo.nii.ac.jp/record/2002035/files/10464054.pdf; https://hdl.handle.net/10228/0002002035; https://kyutech.repo.nii.ac.jp/records/2002035 |
| Rights: |
Copyright (c) 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Accession Number: |
edsbas.947CF6B6 |
| Database: |
BASE |