Quantifying morphological similarity in traditional villages through prototype-driven explainable deep learning

Quantifying morphological similarity in traditional villages through prototype-driven explainable deep learning | 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 Article Quantifying morphological similarity in traditional villages through prototype-driven explainable deep learning Yeting Bu¹, Yu Gu¹, Hailong Zhao¹, Xinhui Wu¹, Weihuan Deng¹, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9230348/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Prototypes are inherited spatial rules that allow village morphology to persist through migration while adapting to local environments. Conventional studies of village morphology remain limited in cross-regional comparison. Remote-sensing-based deep learning enables large-scale quantification of morphological features, yet without prototype theory, inherited features and adaptive changes remain difficult to distinguish. To address this gap, we propose a prototype-driven explainable deep learning framework to quantify morphological similarity in traditional villages by comparing real village samples with ideal prototypes. Evidence from Guangdong indicates that villages with high prototype similarity are spatially consistent with established historical-geographical knowledge. This framework advances the application of explainable AI in geography and architecture and offers a new method for studying traditional village morphology, with potential applications in migration-history research and heritage screening. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 06 May, 2026 Reviews received at journal 05 May, 2026 Reviews received at journal 03 May, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers invited by journal 19 Apr, 2026 Editor assigned by journal 27 Mar, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 26 Mar, 2026 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-9230348","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":627516419,"identity":"f6eefcf3-8ad7-46cc-9e0f-932318c068e7","order_by":0,"name":"Yeting Bu¹","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yeting","middleName":"","lastName":"Bu¹","suffix":""},{"id":627516420,"identity":"9affa3af-6a60-4de3-aa10-487d96347b72","order_by":1,"name":"Yu Gu¹","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Gu¹","suffix":""},{"id":627516421,"identity":"b3b39b7f-ea83-4e38-9967-6c6025e14aa3","order_by":2,"name":"Hailong Zhao¹","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Hailong","middleName":"","lastName":"Zhao¹","suffix":""},{"id":627516422,"identity":"80c2e6ea-2cec-4da8-b5c0-e07fdee1c79f","order_by":3,"name":"Xinhui Wu¹","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Xinhui","middleName":"","lastName":"Wu¹","suffix":""},{"id":627516427,"identity":"04ff22e0-9728-4b2f-bdf3-c2aa9d9d1d84","order_by":4,"name":"Weihuan Deng¹","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Weihuan","middleName":"","lastName":"Deng¹","suffix":""},{"id":627516429,"identity":"57be4738-f28b-4eb6-a699-6b050f0f8ef6","order_by":5,"name":"Xun Li¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACPmYQWcHAwMYOF0vAr4UNrOUMjEGUFhDB2AYkiNfCzmMmXThvmzzQhcyfef4cZuBnzzFg+LkDn8PY0qRnbrtt2MbMwCbN23aYQbLnjQFj7xl8WpiPSfNuu80I0sLM23CYweBGjgEz2Kk4tTC2SfPOuW3fBnOYPWEtIFsabicCtTBI87ABbZEgqIUt2Zrn2O3kNqAyyblt6TwSZ54VHOzFo4Wf/4zhbZ6a27bz25sPf3jzx1qOvz1544OfeLQgAcYGJh4GBh4Q8wBRGsCafhCtdBSMglEwCkYSAACHPj/2YRmzDQAAAABJRU5ErkJggg==","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Xun","middleName":"","lastName":"Li¹","suffix":""}],"badges":[],"createdAt":"2026-03-26 07:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9230348/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9230348/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108007277,"identity":"aa4f0c5d-6df3-47c9-83bf-eb61bac42884","added_by":"auto","created_at":"2026-04-28 12:59:18","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1639608,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptBuetal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9230348/v1_covered_c03ad8ff-afd9-4b2c-9279-8ca950d4a73b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eQuantifying morphological similarity in traditional villages through prototype-driven explainable deep learning\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"npj-heritage-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hsci","sideBox":"Learn more about [Heritage Science](http://heritagesciencejournal.springeropen.com)","snPcode":"40494","submissionUrl":"https://submission.nature.com/new-submission/40494/3","title":"npj Heritage Science","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9230348/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9230348/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrototypes are inherited spatial rules that allow village morphology to persist through migration while adapting to local environments. Conventional studies of village morphology remain limited in cross-regional comparison. Remote-sensing-based deep learning enables large-scale quantification of morphological features, yet without prototype theory, inherited features and adaptive changes remain difficult to distinguish. To address this gap, we propose a prototype-driven explainable deep learning framework to quantify morphological similarity in traditional villages by comparing real village samples with ideal prototypes. Evidence from Guangdong indicates that villages with high prototype similarity are spatially consistent with established historical-geographical knowledge. This framework advances the application of explainable AI in geography and architecture and offers a new method for studying traditional village morphology, with potential applications in migration-history research and heritage screening.\u003c/p\u003e","manuscriptTitle":"Quantifying morphological similarity in traditional villages through prototype-driven explainable deep learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-27 23:14:19","doi":"10.21203/rs.3.rs-9230348/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-06T21:06:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T02:37:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T15:11:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213660536543598374063082707518701178351","date":"2026-04-21T07:08:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"75839024342616346028346678776989771820","date":"2026-04-20T03:21:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-19T21:46:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-27T10:41:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-27T10:40:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Heritage Science","date":"2026-03-26T06:52:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-heritage-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hsci","sideBox":"Learn more about [Heritage Science](http://heritagesciencejournal.springeropen.com)","snPcode":"40494","submissionUrl":"https://submission.nature.com/new-submission/40494/3","title":"npj Heritage Science","twitterHandle":"@SpringerOpen","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cba62fb2-f20f-4100-bd61-a58d6f0e5585","owner":[],"postedDate":"April 27th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-06T21:06:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T02:37:09+00:00","index":17,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T15:11:17+00:00","index":16,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T21:09:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-27 23:14:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9230348","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9230348","identity":"rs-9230348","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}