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

Guy Amir; Ziv Freund; Guy Katz; Elad Mandelbaum; Idan Refaeli

doi:10.1007/978-3-031-27481-7_38

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

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

^*Corresponding author for this work

The Rachel and Selim Benin School of Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 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 language	English
Title of host publication	Formal Methods - 25th International Symposium, FM 2023, Proceedings
Editors	Marsha Chechik, Joost-Pieter Katoen, Martin Leucker
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	648-656
Number of pages	9
ISBN (Print)	9783031274800
DOIs	https://doi.org/10.1007/978-3-031-27481-7_38
State	Published - 2023
Event	25th International Symposium on Formal Methods, FM 2023 - Lübeck, Germany Duration: 6 Mar 2023 → 10 Mar 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14000 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Symposium on Formal Methods, FM 2023
Country/Territory	Germany
City	Lübeck
Period	6/03/23 → 10/03/23

Bibliographical note

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

Access to Document

10.1007/978-3-031-27481-7_38

Cite this

Amir, G., Freund, Z., Katz, G., Mandelbaum, E., & Refaeli, I. (2023). veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System. In M. Chechik, J.-P. Katoen, & M. Leucker (Eds.), Formal Methods - 25th International Symposium, FM 2023, Proceedings (pp. 648-656). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14000 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-27481-7_38

Amir, Guy ; Freund, Ziv ; Katz, Guy et al. / veriFIRE : Verifying an Industrial, Learning-Based Wildfire Detection System. Formal Methods - 25th International Symposium, FM 2023, Proceedings. editor / Marsha Chechik ; Joost-Pieter Katoen ; Martin Leucker. Springer Science and Business Media Deutschland GmbH, 2023. pp. 648-656 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Amir, G, Freund, Z, Katz, G, Mandelbaum, E & Refaeli, I 2023, veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System. in M Chechik, J-P Katoen & M Leucker (eds), Formal Methods - 25th International Symposium, FM 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14000 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 648-656, 25th International Symposium on Formal Methods, FM 2023, Lübeck, Germany, 6/03/23. https://doi.org/10.1007/978-3-031-27481-7_38

veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System. / Amir, Guy; Freund, Ziv; Katz, Guy et al.
Formal Methods - 25th International Symposium, FM 2023, Proceedings. ed. / Marsha Chechik; Joost-Pieter Katoen; Martin Leucker. Springer Science and Business Media Deutschland GmbH, 2023. p. 648-656 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14000 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Amir G, Freund Z, Katz G, Mandelbaum E, Refaeli I. veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System. In Chechik M, Katoen JP, Leucker M, editors, Formal Methods - 25th International Symposium, FM 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 648-656. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-27481-7_38