Demonstration of Wildfire Detection Using Image Classification Onboard CubeSat

Azami, M. H.; Örger, Necmi Cihan; Schulz, Victor Hugo; Kitsune,; Cho, Mengu

doi:10.1109/igarss47720.2021.9553108

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…However, downloading the images to a ground station usually takes hours. To mitigate this challenge, Azami et al, proposed k-nearest and CNN deep-learning (DL) models for image classification implemented on a Raspberry Pi [96]. The runtime for this RPi unit was optimized to minimize the power consumption and was integrated on to a KITSUNE 6U CubeSAT.…”

Section: Satellite Surveillancementioning

confidence: 99%

“…The KITSUNE uses a Sony IMX342 color sensor that generates the images fed into the DL model for classification. Using this setup, the group was able to achieve classification accuracy of above 95% using networks like ShallowNet and LeNet [96,97]. Another recent example is Orora Technologies, which specifically designs 3U CubeSats furnished with IR cameras for finding and monitoring wildfires from space [98].…”

Section: Satellite Surveillancementioning

confidence: 99%

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Early Wildfire Detection Technologies in Practice—A Review

Mohapatra

Trinh

2022

Sustainability

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As fires grow in intensity and frequency each year, so has the resistance from their anthropic victims in the form of firefighting technology and research. Although it is impossible to completely prevent wildfires, the potential devastation can be minimized if fires are detected and precisely geolocated while still in their nascent phases. Furthermore, automated approaches without human involvement are comparatively more efficient, accurate and capable of monitoring extremely remote and vast areas. With this specific intention, many research groups have proposed numerous approaches in the last several years, which can be grouped broadly into these four distinct categories: sensor nodes, unmanned aerial vehicles, camera networks and satellite surveillance. This review paper discusses notable advancements and trends in these categories, with subsequent shortcomings and challenges. We also describe a technical overview of common prototypes and several analysis models used to diagnose a fire from the raw input data. By writing this paper, we hoped to create a synopsis of the current state of technology in this emergent research area and provide a reference for further developments to other interested researchers.

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Section: Satellite Surveillancementioning

confidence: 99%

Section: Satellite Surveillancementioning

confidence: 99%