veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System

Guy Amir*, Ziv Freund, Guy Katz, Elad Mandelbaum, Idan Refaeli

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

In this short paper, we present our ongoing work on the veriFIRE project—a collaboration between industry and academia, aimed at using verification for increasing the reliability of a real-world, safety-critical system. The system we target is an airborne platform for wildfire detection, which incorporates two deep neural networks. We describe the system and its properties of interest, and discuss our attempts to verify the system’s consistency, i.e., its ability to continue and correctly classify a given input, even if the wildfire it describes increases in intensity. We regard this work as a step towards the incorporation of academic-oriented verification tools into real-world systems of interest.

Original languageAmerican English
Title of host publicationFormal Methods - 25th International Symposium, FM 2023, Proceedings
EditorsMarsha Chechik, Joost-Pieter Katoen, Martin Leucker
PublisherSpringer Science and Business Media Deutschland GmbH
Pages648-656
Number of pages9
ISBN (Print)9783031274800
DOIs
StatePublished - 2023
Event25th International Symposium on Formal Methods, FM 2023 - Lübeck, Germany
Duration: 6 Mar 202310 Mar 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14000 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference25th International Symposium on Formal Methods, FM 2023
Country/TerritoryGermany
CityLübeck
Period6/03/2310/03/23

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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