Deep learning for aircraft emergency landing identification: A New Moroccan Terrain Dataset Case Study

Deep learning for aircraft emergency landing identification: A New Moroccan Terrain Dataset Case Study | 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 Deep learning for aircraft emergency landing identification: A New Moroccan Terrain Dataset Case Study Bouzaachane Khadija, EL Guarmah EL Mahdi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6055501/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the aviation industry, forced landings are undesirable occurrences that may occur to an aircraft while it is in flight. They may result from unanticipated circumstances, or engine failures. One approach that could be used to find prospective emergency landing locations for airplanes is image segmentation. Accurate segmentation should enhance aviation safety procedures generally by improving the efficient identification of safe landing zones. The accurate identification of safe landing places is crucial to aviation safety in emergency situations. Using a pixel-wise labeled dataset, this paper introduces a deep learning framework for semantic segmentation with the goal of identifying emergency landing zones in Morocco [1]. With an emphasis on terrain features like surface type, slope, and obstructions, the study assesses cutting-edge architectures such as DeepLabV3+, SegFormer, Faster R-CNN, and U-Net. To improve model generalization, a strong preparation pipeline that uses data augmentation approaches is used. Metrics like F1-score, recall, precision, and Dice coefficients show how effective the model is, and experimental findings show notable gains in segmentation accuracy. Critical gaps in the literature have prompted our investigation, especially with regard to aircraft and emergency runways, which have received relatively little attention. Since the urgent landing objective is a point rather than a piste, the majority of the work that has been published thus far is specifically for UAVs, ([2]-[7]). The results highlight the possibility of incorporating these techniques for safer emergency landings into autonomous aircraft systems. Hybrid models will be investigated in future studies, and the application will be expanded to include more varied datasets. Artificial Intelligence and Machine Learning Aeronautics and Astronautics Forced landings emergency landing site detection DeepLabV3+ SegFormer U-Net faster R-CNN aviation safety Full Text Additional Declarations The authors declare potential competing interests as follows: We have no competing interests to declare. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6055501","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":417435977,"identity":"816287dd-d893-4dea-977f-2f505167df18","order_by":0,"name":"Bouzaachane Khadija","email":"","orcid":"","institution":"1Department of Computer Sciences, L2IS Laboratory,, Faculty of Sciences and Technology, Cadi Ayyad University , 112 Bd Abdelkrim Al Khattabi, Marrakech, 40000, Morocco","correspondingAuthor":false,"prefix":"","firstName":"Bouzaachane","middleName":"","lastName":"Khadija","suffix":""},{"id":417435978,"identity":"32cde524-d4d5-4f52-b58c-58571b05ba3a","order_by":1,"name":"EL Guarmah EL Mahdi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYPCCA3Jg8gEpWozBZAIpWhIbQBRRWuQjkp99ulFzJ31+2OGHQFvs5HQbCGgxvJFmPDvn2LPcjbfTDIBako3NDhDSMiPBmDmH7XDuxtkJIC0HErcR1pL+mTnn3+F0w9npH4jTIi+RY8yc23Y4QV46h0hbDHjeFDPn9h023CCdU3AgwYAIv8i3p29mzvl2WF5+dvrmDx8q7OQIajE4gMIwIKAcbEsDOmMUjIJRMApGAToAANn+SbVa7WrHAAAAAElFTkSuQmCC","orcid":"","institution":"Mathematics and Informatics Department, L2IS Laboratory-Cadi Ayyad University, Royal Air School of Aeronautics, Menara, Marrakech, 40160, Morocco.","correspondingAuthor":true,"prefix":"","firstName":"EL","middleName":"Guarmah EL","lastName":"Mahdi","suffix":""}],"badges":[],"createdAt":"2025-02-18 11:02:15","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6055501/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6055501/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76726994,"identity":"42f563f6-4ca8-4580-a082-9c3535cf7728","added_by":"auto","created_at":"2025-02-20 05:41:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":822816,"visible":true,"origin":"","legend":"","description":"","filename":"MainPaper1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6055501/v1_covered_57609154-15f0-4d5a-8ebf-5d12347425d8.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: We have no competing interests to declare.","formattedTitle":"\u003cp\u003eDeep learning for aircraft emergency landing identification: A New Moroccan Terrain Dataset Case Study\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Cadi Ayyad University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Forced landings, emergency landing site detection, DeepLabV3+, SegFormer, U-Net, faster R-CNN, aviation safety","lastPublishedDoi":"10.21203/rs.3.rs-6055501/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6055501/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the aviation industry, forced landings are undesirable occurrences that may occur to an aircraft while it is in flight. 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