Near Real-Time Wildfire Management Using Distributed Satellite System

Kathiravan Thangavel, Dario Spiller, Roberto Sabatini, Pier Marzocca, Marco Esposito

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Climate action (SDG-13) is an integral part of the Sustainable Development Goals (SDGs) set by the United Nations (UN), and wildfire is one of the catastrophic events related to climate change. Large-scale forest fires have drastically increased in frequency and size in recent years in Australia and other nations. These wildfires endanger the forests and urban areas of the world, demolish vast amounts of property, and frequently result in fatalities. There is a requirement for real-time/near real-time catastrophic event monitoring of fires due to their growing frequency. In order to effectively monitor disaster events, it will be feasible to manage them in real time or near real time due to the advent of the Distributed Satellite System (DSS). This research examines the possible applicability of DSS for wildfire surveillance. For spacecraft to continually monitor the dynamically changing environment, satellite missions must have broad coverage and revisit intervals that DSS can fulfill. A feasibility analysis, as well as a model and scenario prototype for a satellite artificial intelligence (AI) system, is included in this letter to enable prompt action and swiftly provide alerts. In our previous research, it is shown that on- board implementation, i.e., data processing utilizing hardware accelerators, is feasible. To enable Trusted Autonomous Satellite Operation (TASO), the same will be included in the proposed DSS architecture, and the outcomes will be provided. To demonstrate the applicability, the suggested DSS architecture will be tested in several geographic locations to demonstrate the system-wide coverage.

Original languageBritish English
Article number5500705
JournalIEEE Geoscience and Remote Sensing Letters
Volume20
DOIs
StatePublished - 2023

Keywords

  • 1-D convolutional neural network (CNN)
  • climate action
  • Distributed Satellite System (DSS)
  • edge computing
  • hardware accelerators
  • real-time monitoring
  • Sustainable Development Goal (SDG)-13
  • Trusted Autonomous Satellite Operation (TASO)
  • wildfire

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