| Title: |
Wildfire detection CubeSat based on convolution neural network |
| Authors: |
Muhammad Hasif Bin Azami; 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; 趙, 孟佑; 754; Cho, Mengu; 60243333; 7401727758; 168 |
| Publisher Information: |
Society of Photo-Optical Instrumentation Engineers |
| 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: |
Wildfire; CubeSat; convolution neural network; total ionizing dose |
| Description: |
Wildfires burn millions of hectares of land every year globally. Most of them are caused by humans, while only 10-15% occur naturally due to the climate change. The hotter weather dries out forests and plants, making them more prone to fire. The “frontline wildfire defense” has fully utilized satellite imagery to monitor, map, and control the fire spread and damage. However, there are three major challenges of using traditional satellite data: (1) the spatial resolution, (2) the temporal resolution, and (3) the downlink and analyzing data on the ground. In recent technology, the satellites are developed into small-size CubeSats that supporting the resolution issues. By exploiting the deep learning (DL) technique, the CubeSat can become sufficiently “intelligent” to detect wildfire events. This paper discusses a potential approach for implementing a Convolution Neural Network (CNN) onboard a CubeSat to sense wildfire. The DL model has been tested on the Camera Controller Board (CCB) embedded with Raspberry Pi Compute Module (RPi CM3+), that interfacing with the imaging mission of a 6U CubeSat named KITSUNE. In addition, the space environment test of radiation Total Ionizing Dose (TID) with functional tests of the board has been discussed. The results have shown no anomaly observed on the RPi while the DL model achieved a 94% overall accuracy with 16 minutes of learning time and 32 seconds of classification time. Hence, the state-of-art processing images onboard CubeSat will improve the valuable downlink data as the limited time window passes through the ground station. ; journal article |
| Document Type: |
other/unknown material |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
Proceedings of SPIE; 11914; 119140K; https://kyutech.repo.nii.ac.jp/record/2002022/files/10464059.pdf; https://hdl.handle.net/10228/0002002022; https://kyutech.repo.nii.ac.jp/records/2002022 |
| Availability: |
https://kyutech.repo.nii.ac.jp/record/2002022/files/10464059.pdf; https://hdl.handle.net/10228/0002002022; https://kyutech.repo.nii.ac.jp/records/2002022 |
| Rights: |
(2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only. |
| Accession Number: |
edsbas.13035254 |
| Database: |
BASE |