{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T12:21:51Z","timestamp":1783686111230,"version":"3.55.0"},"reference-count":59,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T00:00:00Z","timestamp":1783641600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Transactions in GIS"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    In this study, we reconstruct multi\u2010decadal Land Use and Land Cover (LULC) dynamics (1990\u20132025) in the Pearl River Delta (PRD) using a deep representation learning\u2013driven, self\u2010supervised framework applied to harmonized Landsat archives. High\u2010confidence pseudo\u2010label cores were derived from percentile\u2010based spectral\u2013phenological rules, refined with robust Median Absolute Deviation (MAD) filtering, then embedded with an era\u2010specific denoising autoencoder and classified with Random Forest (RF). Over the span of four Landsat generations, classification performance proved consistent, with overall accuracy frequently above 0.80 and peaking at 0.914\u2009\u00b1\u20090.060 during the Landsat\u20109 era. The PRD noticed substantial and diverse land redistribution between 1990 and 2025. The highest relative expansion was in urban land, which increased from 1931.43 to 4547.26\u2009km\n                    <jats:sup>2<\/jats:sup>\n                    (+2615.83\u2009km\n                    <jats:sup>2<\/jats:sup>\n                    ; +135.4%), and in forest area, which increased from 15,863.85 to 20,509.68\u2009km\n                    <jats:sup>2<\/jats:sup>\n                    (+4645.83\u2009km\n                    <jats:sup>2<\/jats:sup>\n                    ; +29.3%). A notable spike in agricultural land was observed, from 10,495.05 to 12,193.43\u2009km\n                    <jats:sup>2<\/jats:sup>\n                    (+1698.38\u2009km\n                    <jats:sup>2<\/jats:sup>\n                    ; +16.2%). On the other hand, the amount of water reduced from 8004.79 to 4675.04\u2009km\n                    <jats:sup>2<\/jats:sup>\n                    (\u22123329.75\u2009km\n                    <jats:sup>2<\/jats:sup>\n                    ; \u221241.6%), and the amount of bare land decreased marginally from 18,041.47 to 17,270.08\u2009km\n                    <jats:sup>2<\/jats:sup>\n                    (\u2212771.39\u2009km\n                    <jats:sup>2<\/jats:sup>\n                    ; &lt;\u20094.3%). A systematic, uneven distribution was also evident in the intensity analysis, with the two main transition pathways being barren\u2010to\u2010water (34.92%) and barren\u2010to\u2010urban (9.29%). These findings demonstrate that self\u2010supervised representation learning (SSRL) enables temporally consistent LULC reconstruction while diagnosing directional land reallocation in highly heterogeneous megaregions.\n                  <\/jats:p>","DOI":"10.1111\/tgis.70346","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T12:08:31Z","timestamp":1783685311000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Representation Learning\u2010Driven Self\u2010Supervised Label Generation for Automated Long\u2010Term Land Use\/Land Cover Classification Using Remote Sensing and\n                    <scp>GIS<\/scp>"],"prefix":"10.1111","volume":"30","author":[{"given":"Jianshe","family":"Wang","sequence":"first","affiliation":[{"name":"School of Land Information and Management Henan College of Surveying and Mapping  Zhengzhou Henan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Shahid","family":"Iqbal","sequence":"additional","affiliation":[{"name":"PMAS Arid Agriculture University  Rawalpindi Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riaz","family":"Sheriff","sequence":"additional","affiliation":[{"name":"Ecoinformatics Lab, Ashoka Trust for Research in Ecology and the Environment  Bengaluru India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2497-358X","authenticated-orcid":false,"given":"Mohammad Suhail","family":"Meer","sequence":"additional","affiliation":[{"name":"HealthGIS Laboratory, Saveetha Medical College and Hospital Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University  Chennai India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aleksandra O.","family":"Utkina","sequence":"additional","affiliation":[{"name":"Institute of Environmental Engineering, RUDN University  Moscow Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7217-7970","authenticated-orcid":false,"given":"Qaisar","family":"Abbas","sequence":"additional","affiliation":[{"name":"Department of Economics, College of Political Science and Economics Korea University  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2656-8679","authenticated-orcid":false,"given":"Rana Muhammad","family":"Zulqernain","sequence":"additional","affiliation":[{"name":"Department of Mathematics Saveetha School of Engineering, SIMATS Thandalam  Chennai India"},{"name":"Department of Computer Engineering Biruni University  Istanbul Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sajjad","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Environmental Sciences COMSATS University Islamabad, Vehari Campus  Vehari 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