An Optimised Satellite Constellation for Forest Fire Detection through Edge Computing

TL;DR

This paper proposes an optimized LEO satellite constellation combined with edge computing for rapid bushfire detection, significantly reducing detection time compared to existing methods.

Contribution

It introduces a novel satellite constellation optimized with NSGA-II and integrates edge computing and inter-satellite communication for fast, in-orbit bushfire detection.

Findings

Detects fires over 5m in length

Achieves detection in 1.39 seconds per image

Faster than existing bushfire detection methods

Abstract

The end of 2019 marked a bushfire crisis for Australia that affected more than 100000km2 of land and destroyed more than 2000 houses. Here, we propose a method of in-orbit bushfire detection with high efficiency to prevent a repetition of this disaster. An LEO satellite constellation is first developed through NSGA-II (Nondominated Sorting Genetic Algorithm II), optimising for coverage over Australia. Then edge computing is adopted to run a bushfire detection algorithm using several constellation satellites as edge nodes to reduce fire detection time. A geostationary satellite is used for inter-satellite communications, such that an image taken by a satellite can be distributed among several satellites for processing. The geostationary satellite also maintains a constant link to the ground, so that a bushfire detection can be reported back without any significant delay. Overall, this system is able to detect fires that span more than 5m in length, and can make detections in 1.39s per image processed. This is faster than any currently available bushfire detection method.