Deep Learning Architectures for Forest Monitoring and Tree Inventory Management: A Review of Computer Vision Applications and Challenges

Deep Learning Architectures for Forest Monitoring and Tree Inventory Management: A Review of Computer Vision Applications and Challenges | 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 Systematic Review Deep Learning Architectures for Forest Monitoring and Tree Inventory Management: A Review of Computer Vision Applications and Challenges Gabriel Osei Forkuo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8908445/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 Traditional forest monitoring depends heavily on manual fieldwork, which limits its spatial and temporal resolution. This paper offers a systematic review of Deep Learning (DL) and Computer Vision (CV) applications in forestry, compiling 178 peer-reviewed articles published from 2011 to 2025. Three critical research gaps namely: (1) the absence of standardized benchmarking protocols across 73% of studies, (2) limited cross-biome transferability with performance degradation of 23–45% when models are applied outside training regions, and (3) minimal adoption of explainable AI methods in 89% of applications were identified. The contribution to artificial intelligence (AI) provides a thorough examination of the transition from traditional convolutional neural networks (CNNs) to advanced vision transformers (ViTs) and graph neural networks (GNNs), highlighting the principles of multi‑modal data fusion and three‑dimensional (3D) feature extraction. In computer vision and engineering, the focus is on automating tree inventory management, particularly individual tree detection (ITD), species identification, and biomass estimation with various remote‑sensing platforms. A quantitative meta‑analysis shows that CNNs achieve a mean species‑classification accuracy of 87.3% (± 6.2%), whereas ViT‑based models reach 95.7% (± 3.1%)—an 8.4% improvement—on multi‑modal datasets (n = 34 studies), though they require 3.2 × more training data. For biomass estimation, fusion methods that combine LiDAR and hyperspectral data yield an R² of 0.89 (± 0.07), a 31% gain over single‑sensor approaches. The integration of data from Unmanned Aerial Vehicles (UAVs) and satellite platforms has significantly improved inventory precision, with benchmarks frequently exceeding 90% accuracy. Nevertheless, practical engineering deployment remains challenged by soft-computing issues: the limited availability of annotated datasets causing overfitting; poor model transferability across ecological regions; and a lack of interpretability. Future work should focus on Explainable Artificial Intelligence (XAI) to map decision boundaries, Generative Adversarial Networks (GANs) for synthetic data generation, and hybrid models for real-time analysis. This review's novel contribution includes: (1) a computational complexity-performance trade-off analysis across architectures, (2) decision framework mapping sensor modalities to forestry applications, and (3) quantified transferability metrics across seven ecological biomes. This review delineates the essential computational steps required to develop robust, deployable models for worldwide sustainable forest management. Forestry Artificial Intelligence and Machine Learning biomass estimation convolutional neural networks explainable artificial intelligence light detection and ranging remote sensing tree species classification unmanned aerial vehicles Full Text Additional Declarations The authors declare no competing interests. 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-8908445","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":593280403,"identity":"8998422e-35f2-4c8c-8e0c-44da73d275a6","order_by":0,"name":"Gabriel Osei Forkuo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYFACHgaGBAYLBvYGBmYgz4YBSLERo0WCgecAWEsaUAszEVoYEFoOAzEBLfLuZ499eFAB1MLe+9iYp+J84nZ2/mOPeRjuyeHSYngmL3lGwhmgFp7jxsk8Z24n7mxmZjfmYSg2xqmlIceYIbFNgsFeIo35MG/b7cQNh5nZJGcwJCQ24NLS/wao5R/QFoiWc4S1yEuAbGmAaEnmbTsA1iLxAY8WA4l3yQwJxyR4eHiOMRvOOZNsDNRibvDBIAGnX+T7cw8z/qixkeNhb2OWeFNhJ7vh/MFnDxIqEnCGmMEBCM2DLo5LA9AWXC4eBaNgFIyCUQAHADWxS35CD5XtAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-8478-8066","institution":"Transilvania University of Brasov","correspondingAuthor":true,"prefix":"","firstName":"Gabriel","middleName":"Osei","lastName":"Forkuo","suffix":""}],"badges":[],"createdAt":"2026-02-18 10:44:04","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8908445/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8908445/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDeep Learning Architectures for Forest Monitoring and Tree Inventory Management: A Review of Computer Vision Applications and Challenges\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Transilvania University of Brsov","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":"biomass estimation, convolutional neural networks, explainable artificial intelligence, light detection and ranging, remote sensing, tree species classification, unmanned aerial vehicles","lastPublishedDoi":"10.21203/rs.3.rs-8908445/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8908445/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraditional forest monitoring depends heavily on manual fieldwork, which limits its spatial and temporal resolution. This paper offers a systematic review of Deep Learning (DL) and Computer Vision (CV) applications in forestry, compiling 178 peer-reviewed articles published from 2011 to 2025. Three critical research gaps namely: (1) the absence of standardized benchmarking protocols across 73% of studies, (2) limited cross-biome transferability with performance degradation of 23\u0026ndash;45% when models are applied outside training regions, and (3) minimal adoption of explainable AI methods in 89% of applications were identified. The contribution to artificial intelligence (AI) provides a thorough examination of the transition from traditional convolutional neural networks (CNNs) to advanced vision transformers (ViTs) and graph neural networks (GNNs), highlighting the principles of multi‑modal data fusion and three‑dimensional (3D) feature extraction. In computer vision and engineering, the focus is on automating tree inventory management, particularly individual tree detection (ITD), species identification, and biomass estimation with various remote‑sensing platforms. A quantitative meta‑analysis shows that CNNs achieve a mean species‑classification accuracy of 87.3% (\u0026plusmn;\u0026thinsp;6.2%), whereas ViT‑based models reach 95.7% (\u0026plusmn;\u0026thinsp;3.1%)\u0026mdash;an 8.4% improvement\u0026mdash;on multi‑modal datasets (n\u0026thinsp;=\u0026thinsp;34 studies), though they require 3.2 \u0026times; more training data. For biomass estimation, fusion methods that combine LiDAR and hyperspectral data yield an \u003cem\u003eR\u0026sup2;\u003c/em\u003e of 0.89 (\u0026plusmn;\u0026thinsp;0.07), a 31% gain over single‑sensor approaches. The integration of data from Unmanned Aerial Vehicles (UAVs) and satellite platforms has significantly improved inventory precision, with benchmarks frequently exceeding 90% accuracy. Nevertheless, practical engineering deployment remains challenged by soft-computing issues: the limited availability of annotated datasets causing overfitting; poor model transferability across ecological regions; and a lack of interpretability. Future work should focus on Explainable Artificial Intelligence (XAI) to map decision boundaries, Generative Adversarial Networks (GANs) for synthetic data generation, and hybrid models for real-time analysis. This review's novel contribution includes: (1) a computational complexity-performance trade-off analysis across architectures, (2) decision framework mapping sensor modalities to forestry applications, and (3) quantified transferability metrics across seven ecological biomes. 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