Dr. Necmi Cihan Örger Profile

Dr. Necmi Cihan Örger

at Kyushu Institute of Technology

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Proceedings Article | 14 November 2021 Presentation + Paper

Wildfire detection CubeSat based on convolution neural network

Muhammad Hasif Bin Azami, Necmi Cihan Orger, Victor Hugo Schulz, Mengu Cho

Proceedings Volume 11914, 119140K (2021) https://doi.org/10.1117/12.2603906

KEYWORDS: Satellites, Neural networks, Convolution, Earth observing sensors, Satellite imaging, Cameras, Systems modeling, Commercial off the shelf technology, Image processing

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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.

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