@inproceedings{0696685034c34fd8bb53783c1f6b410f,
title = "veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System",
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{\textquoteright}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.",
author = "Guy Amir and Ziv Freund and Guy Katz and Elad Mandelbaum and Idan Refaeli",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 25th International Symposium on Formal Methods, FM 2023 ; Conference date: 06-03-2023 Through 10-03-2023",
year = "2023",
doi = "https://doi.org/10.1007/978-3-031-27481-7_38",
language = "الإنجليزيّة",
isbn = "9783031274800",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "648--656",
editor = "Marsha Chechik and Joost-Pieter Katoen and Martin Leucker",
booktitle = "Formal Methods - 25th International Symposium, FM 2023, Proceedings",
address = "ألمانيا",
}