{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T20:00:48Z","timestamp":1784145648646,"version":"3.55.0"},"reference-count":72,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Google Earth Engine (GEE) has become one of the most widely used platforms for Land Use and Land Cover (LULC) research, offering cloud-based access to petabyte-scale datasets and scalable analytical tools. While earlier reviews provided valuable overviews of data and applications, this study synthesizes 72 selected articles published between 2016 and February 2025 to examine the evolution of GEE\u2013LULC research. Results show exponential growth in publications, with Landsat and Sentinel imagery dominating datasets and Random Forest (RF) and Support Vector Machine (SVM) remaining the most common classifiers. Geographically, output is concentrated in China and India, reflecting regional leadership in GEE adoption. Despite its strengths, GEE faces persistent challenges, including memory limits, restricted support for advanced Deep Learning (DL), and reliance on labeled data. Promising directions include integrating few-shot semantic segmentation and hybrid workflows combining GEE scalability with local Graphics Processing Unit (GPU) computing. By bridging platform-focused and application-focused studies, this review provides a comprehensive synthesis of GEE\u2013LULC research and outlines actionable pathways for advancing scalable and Artificial Intelligence (AI)-enabled geospatial analysis.<\/jats:p>","DOI":"10.3390\/ijgi14110416","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T08:53:25Z","timestamp":1761296005000},"page":"416","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Review of Google Earth Engine for Land Use and Land Cover Change Analysis: Trends, Applications, and Challenges"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7985-3239","authenticated-orcid":false,"given":"Bader","family":"Alshehri","sequence":"first","affiliation":[{"name":"School of Surveying and Built Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia"},{"name":"General Authority for Survey and Geospatial Information (GEOSA), Riyadh 12611, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Surveying and Built Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0487-9193","authenticated-orcid":false,"given":"Xiaoye","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Surveying and Built Environment, University of Southern Queensland, Toowoomba, QLD 4350, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s12040-024-02305-3","article-title":"Assessment of Land Use-Land Cover Dynamics and Its Future Projection through Google Earth Engine, Machine Learning and QGIS-MOLUSCE: A Case Study in Jagatsinghpur District, Odisha, India","volume":"133","author":"Bathe","year":"2024","journal-title":"J. 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