Transparent Insights into AI: Analyzing CNN Architecture through LIME-Based Interpretability for Land Cover Classification

Transparent Insights into AI: Analyzing CNN Architecture through LIME-Based Interpretability for Land Cover Classification | 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 Transparent Insights into AI: Analyzing CNN Architecture through LIME-Based Interpretability for Land Cover Classification Pushpalata Pujari, Himanshu Sahu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6209890/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 The realization that complex deep learning models may make morally significant decisions has led to a growing interest in Explainable Artificial Intelligence (XAI), whose primary concern is understanding why it made particular predictions or recommendations. This paper investigates the effectiveness of different Convolutional Neural Network (CNN) architectures that are employed on satellite images from the Airbus SPOT6 and SPOT7 Datasets. The evaluated designs are MobileNetV2, Alex Net, ResNet50, VGG16, DenseNet, Inception-ResNet v2, InceptionV3, XceptionNet, and EfficientNet. MobileNetV2 showed best in other classification parameters such as accuracy of 99.20%, precision rate of 99.39%, recall rate of 99.00 %, F1 score to be at a maximum with 99.16 % and an AUC (Area Under the Curve) to be detected across all categories correctly at 99.96%. The research study uses LIME (Local Interpretable Model-agnostic Explanations) to examine MobileNetV2, a system that uses satellite images to classify wind turbines. LIME creates interpretable models, such as white box models, to estimate complex predictions. This helps identify key factors in classification, making the model more interpretable. The study uses heatmaps and attention maps to identify areas in Airbus SPOT satellite images that impact MobileNet classifications. This enhances trust in the AI system and opens up opportunities for understanding model behaviour. Explainable Artificial Intelligence (XAI) CNN Image Classification LIME Heatmaps Attention Map Feature Importance Full Text Additional Declarations No competing interests reported. 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. 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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-6209890","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":428080737,"identity":"a4f57496-ed90-484d-8867-babda548ba9f","order_by":0,"name":"Pushpalata Pujari","email":"","orcid":"","institution":"Guru Ghasidas Vishwavidyalaya","correspondingAuthor":false,"prefix":"","firstName":"Pushpalata","middleName":"","lastName":"Pujari","suffix":""},{"id":428080741,"identity":"26b6e49f-d5dc-4df8-a43f-b51a38df213c","order_by":1,"name":"Himanshu Sahu","email":"data:image/png;base64,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","orcid":"","institution":"Guru Ghasidas Vishwavidyalaya","correspondingAuthor":true,"prefix":"","firstName":"Himanshu","middleName":"","lastName":"Sahu","suffix":""}],"badges":[],"createdAt":"2025-03-12 08:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6209890/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6209890/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85879878,"identity":"db340276-158a-4047-a6ca-bfad4fdc390f","added_by":"auto","created_at":"2025-07-02 15:53:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1798643,"visible":true,"origin":"","legend":"","description":"","filename":"XAIFinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6209890/v1_covered_2343ef38-7673-4664-afbb-ded41c1ff374.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transparent Insights into AI: Analyzing CNN Architecture through LIME-Based Interpretability for Land Cover Classification ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"Explainable Artificial Intelligence (XAI), CNN, Image Classification, LIME, Heatmaps, Attention Map, Feature Importance","lastPublishedDoi":"10.21203/rs.3.rs-6209890/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6209890/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe realization that complex deep learning models may make morally significant decisions has led to a growing interest in Explainable Artificial Intelligence (XAI), whose primary concern is understanding why it made particular predictions or recommendations. 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