A Fire Management Intelligent System for the Brazilian Cerrado Biome based on a Deep Learning Two Phase Detection Method

A Fire Management Intelligent System for the Brazilian Cerrado Biome based on a Deep Learning Two Phase Detection Method | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Fire Management Intelligent System for the Brazilian Cerrado Biome based on a Deep Learning Two Phase Detection Method Natalia Borges, Livia Fonseca, Priscila Solis Barreto, Eduardo Alchieri, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4865999/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Feb, 2025 Read the published version in Journal of Reliable Intelligent Environments → Version 1 posted 10 You are reading this latest preprint version Abstract The significant advancements in the fields of image processing and machine learning to implement systems for early accurately smoke and fire detection still present research challenges for certain biomes. The cerrado is a fire-prone ecosystem that covers a large area in South America, where videobased fire smoke detection using cameras could be more efficient than sensor based systems. This work presents the SEMFOGO-DF fire monitoring system, a solution composed of a distributed processing architecture and a deep-learning computer vision algorithm for smoke detection and emergency alert generation. The solution performs smoke detection in image sequences using a two-phase algorithm that includes automatic zoom operations to confirm smoke predictions. The first phase analyzes image sequences and classifies regions of the image with a high probability of fire and in the second phase, a smoke classification is conducted on a zoomed image. The proposed solution underwent an experimental evaluation in the Brazilian Federal District, which is situated in the Midwest region of Brazil. Experimental results show that the two-phase algorithm can consistently reduce the number of false alerts generated by the first phase alone, with a relatively low reduction in the detection rate. The development of the solution also allowed the creation of two novel datasets. The first dataset consists of image sequences of the cerrado biome, annotated with smoke contours. The second dataset includes zoomed images of the cerrado landscape, annotated with labels indicating smoke and nonsmoke occurrences. These datasets provide a robust representation of the cerrado biome and have the potential to aid other research groups working on similar developments. Smoke fire monitoring Machine Learning Intelligent System Cerrado datasets Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Feb, 2025 Read the published version in Journal of Reliable Intelligent Environments → Version 1 posted Editorial decision: Revision requested 18 Nov, 2024 Reviews received at journal 11 Nov, 2024 Reviewers agreed at journal 05 Nov, 2024 Reviews received at journal 18 Oct, 2024 Reviewers agreed at journal 10 Oct, 2024 Reviewers agreed at journal 16 Sep, 2024 Reviewers invited by journal 12 Aug, 2024 Editor assigned by journal 07 Aug, 2024 Submission checks completed at journal 06 Aug, 2024 First submitted to journal 06 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4865999","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":347789427,"identity":"37f2d7a9-e8db-4c10-9efc-fab7d589dfb2","order_by":0,"name":"Natalia Borges","email":"","orcid":"","institution":"University of Brasília","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"","lastName":"Borges","suffix":""},{"id":347789428,"identity":"36847451-a0b9-4bc4-b4c1-fe5f7a678940","order_by":1,"name":"Livia Fonseca","email":"","orcid":"","institution":"University of 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