[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124358-en":3,"doc-seo-124358-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},124358,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Land Use Analysis Using Machine Learning Based on Cloud Computing Platform - Study with GEE and Random Forest","Land use analysis supports regional planning and environmental monitoring by clarifying land use patterns and changes. This study applies machine learning on a cloud computing platform, Google Earth Engine (GEE), to process spatial data efficiently and at scale. It uses GEE with a Random Forest classifier to identify land use types and estimate classification accuracy. Inputs include Bangkalan Regency administrative boundaries and Landsat 8 imagery for 2022, with training and testing samples.","Land Use Analysis Using Machine-Learning Based on Cloud Computing  \nPlatform  \nSyukur Toha Prasetyo, Fahmi Arief Rahman*, Sinar Suryawati, Slamet Supriyadi, Eko Setiawan  \n(Received December 2023/Accepted July 2025)  \nABSTRACT  \nLand use analysis can provide a foundation for successful and efficient regional planning and environmental monitoring. The application of machine-learning on a cloud computing platform (Google Earth Engine, GEE) in land use analysis enables efficient and rapid processing of spatial data on a wide scale. It overcomes the constraints inherent in conventional approaches. The purpose of this study was to identify land use and estimate its level of accuracy using GEE and a Random Forest machine-learning method. The data utilized were the administrative boundaries of Bangkalan Regency (1:25,000) and Landsat 8 SR L2 C2 T1 satellite images from 2022. Satellite image analysis using the Random Forest algorithm on the GEE platform with the JavaScript API, including masking, cloud masking, class and sampling, training, and testing sample data. Land use study using the Random Forest algorithm yielded the following results in order of area: vegetation 65,040.39 ha (49.98%), agricultural land 31,817.16 ha (24.45%), settlements 20,578.05 ha (15.81%), open land 6,683.94 ha (5.14%), and water bodies 6,021.09 ha (4.63%) . The accuracy test in GEE revealed an overall accuracy (OA) of 91.39% and a kappa score of 88.39%, or 0.88. At the sametime, validation in the field gave an OA of 88.68% and a Kappa of 85.53%. The findings of this study can be applied to land use evaluation and fundamental decision-making.  \nKeywords: land use, random forest, geographic information system, remote sensing  \nINTRODUCTION  \nLand is a resource that can change function due toa variety of reasons, including population expansion, terrain changes, land value, and community socioeconomic conditions (Susanti et al. 2020) . Thus, land use analysis can be useful in regional planning and environmental monitoring (Zulfajri et al. 2021) . Proper land use analysis is critical for understanding patterns and changes to promote more effective, efficient, and sustainable planning. Land use is defined as human activities that are directly tied to land and may be easily understood through mapping land use classification (Lestari and Arsyad 2018) . Land use classification can be done using either conventional methods or remote sensing. Remote sensing utilizing satellite imagery is more effective and efficient than previous methods (Firmawan and Nirmala 2021) .  \nRemote sensing is commonly utilized in land use analysis, but it has not been widely integrated with cloud computing platform-based machine-learning algorithms like Google Earth Engine (GEE) . The platform provides efficient and rapid processing of spatial data at scale, overcoming the limits of traditional methods that necessitate high-performance equipment, substantial data storage, and sophisticated  \nStudy Program of Agrotechnology, Faculty of Agriculture, University of Trunojoyo Madura , Bangkalan 69162  \n* Corresponding Author:  \nEmail: [fahmi.rahman@trunojoyo.ac.id](fahmi.rahman@trunojoyo.ac.id)  \nanalytic processes. Arifin (2017) conducted research on land use changes following the construction of the Suramadu bridge in Bangkalan District, and Rahman and Adiputra (2022) studied the conversion of agricultural land into settlements in Bangkalan Regency, both using conventional methods that require high-specification devices, satellite imagery, and manual interpretation for a long time. The GEE platform is more convenient than traditional methods of processing data from various satellites in terms of speed, flexibility, and cost, and it includes machinelearning algorithms such as Random Forest (RF) to speed up the classification process (Febriani et al. 2022; Suryono et al. 2022) .  \nThe purpose of this study was to categorize land use and determine the accuracy of classification results using GE","cbCaiqPgAuidJGK7","https://ap.wps.com/l/cbCaiqPgAuidJGK7","pdf",970676,1,"English","en",105,"# Abstract\n# Introduction\n# Methods\n## Research Site\n## Tools and Materials\n## Data Engineering and Analysis\n### Google Earth Engine\n### Masking and cloud masking","[{\"question\":\"What is the main purpose of using GEE and Random Forest for land use analysis?\",\"answer\":\"To categorize land use and determine the accuracy of classification results using Google Earth Engine with a Random Forest machine-learning method.\"},{\"question\":\"What data were used in the study?\",\"answer\":\"Administrative boundaries of Bangkalan Regency (scale 1:25,000) and Landsat 8 SR L2 C2 T1 satellite images from 2022 were used.\"},{\"question\":\"How accurate were the land use classification results?\",\"answer\":\"Overall accuracy in GEE reached 91.39% with a kappa of 88.39%, while field validation produced an OA of 88.68% and a kappa of 85.53%.\"}]","Land Use Analysis Using Machine Learning Based on Cloud Computing Platform - Study with GEE and Random Forest | PDF",1785821804,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"land-use-analysis-using-machine-learning-based-on-cloud-computing-platform-study-with-gee-and-random-forest","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/land-use-analysis-using-machine-learning-based-on-cloud-computing-platform-study-with-gee-and-random-forest/124358/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What is the main purpose of using GEE and Random Forest for land use analysis?","Question",{"text":74,"@type":75},"To categorize land use and determine the accuracy of classification results using Google Earth Engine with a Random Forest machine-learning method.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What data were used in the study?",{"text":79,"@type":75},"Administrative boundaries of Bangkalan Regency (scale 1:25,000) and Landsat 8 SR L2 C2 T1 satellite images from 2022 were used.",{"name":81,"@type":72,"acceptedAnswer":82},"How accurate were the land use classification results?",{"text":83,"@type":75},"Overall accuracy in GEE reached 91.39% with a kappa of 88.39%, while field validation produced an OA of 88.68% and a kappa of 85.53%.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]