[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128480-en":3,"doc-seo-128480-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128480,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Multitemporal Analysis in Google Earth Engine - Detecting Urban Changes Using Optical Data and Machine Learning Algorithms","The work presents a multitemporal analysis on the Google Earth Engine (GEE) platform to detect changes in urban areas using optical satellite data and targeted machine learning algorithms. Cairo City (Egypt) is used as a case study, with a region of interest analyzed from July 2013 to July 2021. Classification and change detection results highlight the method’s ability to distinguish changed from unchanged urban areas. The study also emphasizes GEE’s effectiveness as a cloud-based solution for managing large volumes of satellite imagery.","Multitemporal analysis in Google Earth Engine for detecting urban changes using optical data and machine learning algorithms  \narXiv :2308 . 11468v1 [ cs .CV] 22 Aug 2023  \n1nd Mariapia Rita Iandolo Engineering Department University of Sannio Benevento, Italy  \n[m.iandolo1](m.iandolo1@studenti.unisannio.it)[@](m.iandolo1@studenti.unisannio.it)[studenti.unisannio.it](m.iandolo1@studenti.unisannio.it)  \n2st Francesca Razzano Engineering Department University of Sannio Benevento, Italy [f.razzano3](f.razzano3@studenti.unisannio.it)[@](f.razzano3@studenti.unisannio.it)[studenti.unisannio.it](f.razzano3@studenti.unisannio.it)  \n3rd Chiara Zarro Aerospace Department Intelligentia srl Benevento, Italy [chiara.zarro@intelligentia.it](chiara.zarro@intelligentia.it)  \n4th G. S. Yogesh  \nEngineering Department East Point College Bangalore, India [gs.yogesh@eastpoint.ac.in](gs.yogesh@eastpoint.ac.in)  \n5th Silvia Liberata Ullo Engineering Department University of Sannio Benevento, Italy [ullo@unisannio.it](ullo@unisannio.it)  \nAbstract—The aim of this work is to perform a multitemporal analysis using the Google Earth Engine (GEE) platform for the detection of changes in urban areas using optical data and specific machine learning (ML) algorithms. As a case study, Cairo City has been identified, in Egypt country, as one of the five most populous megacities of the last decade in the world. Classification and change detection analysis of the region of interest (ROI) have been carried out from July 2013 to July 2021. Results demonstrate the validity of the proposed method in identifying changed and unchanged urban areas over the selected period. Furthermore, this work aims to evidence the growing significance of GEE as an efficient cloud-based solution for managing large quantities of satellite data.  \nIndex Terms—Optical and SAR data classification, Machine Learning algorithms, change detection, Google Earth Engine, Earth Observation, Landsat-8.  \nI. INTRODUCTION  \nNowadays good urban planning cannot be based only on classical methods, but it is important to take advantage of the advanced techniques which are catching on the remote sensing (RS) field. Among them, ML and Deep Learning (DL) techniques are increasingly playing a crucial role in handling huge amounts of satellite data and enhancing results in terms of classification analysis, land use monitoring, and natural phenomena detection [1], [2], [3], just to give some examples. Measuring and understanding the nature and entity of changes affecting urban and non-urban territories are crucial in order to determine their future expansion and impact in terms of environmental and economic issues. For this reason, extensive studies and research have been carried out on detecting urban changes and on using their results as valuable information to support Governments and Municipalities in taking better decisions. In [4], change detection is applied to Newly Constructed Areas (NCA) as the first step in the development monitoring  \nof urban areas, through a ML approach. Various spectral indices for a rapid and accurate built land classification are proposed in [5] . Their performance is examined and compared in the classification and detection of land changes when Landsat-images7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat-8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) are used as satellite images. In [6], authors deal with the change detection of the urban area of Bauchi which is one of the cities in the northeastern part of Nigeria that has witnessed a huge expansion due to rapid urbanization. In particular, they compare three change detection algorithms, supervised, unsupervised, and post-classification comparison, the latter for achieving a ”from-to” evaluation, and demonstrate that the supervised classification produces the best results in terms of overall precision in the reference years.  \nIn our paper, we aim to perform a multitemporal analysis of optical satellite data","cbCaihJ5pHxR9vYd","https://ap.wps.com/l/cbCaihJ5pHxR9vYd","pdf",4121552,1,4,"English","en",105,"# Abstract\n# Introduction\n# GEE and Data Sources\n## Google Earth Engine","[{\"question\":\"What is the main goal of this research?\",\"answer\":\"To perform multitemporal analysis in Google Earth Engine to detect urban changes using optical data and specific machine learning algorithms.\"},{\"question\":\"Which case study and time period are used for the analysis?\",\"answer\":\"Cairo City in Egypt is analyzed, covering changes from July 2013 to July 2021.\"},{\"question\":\"How does the approach evaluate changed vs unchanged urban areas?\",\"answer\":\"It carries out supervised classification and change detection on the region of interest, producing results that distinguish changed and unchanged urban areas over the selected period.\"}]","Multitemporal Analysis in Google Earth Engine - Detecting Urban Changes Using Optical Data and Machine Learning Algorithms | PDF",1786001307,10,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"multitemporal-analysis-in-google-earth-engine-detecting-urban-changes-using-optical-data-and-machine-learning-algorithms","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/multitemporal-analysis-in-google-earth-engine-detecting-urban-changes-using-optical-data-and-machine-learning-algorithms/128480/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-24","2026-08-06",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this research?","Question",{"text":75,"@type":76},"To perform multitemporal analysis in Google Earth Engine to detect urban changes using optical data and specific machine learning algorithms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which case study and time period are used for the analysis?",{"text":80,"@type":76},"Cairo City in Egypt is analyzed, covering changes from July 2013 to July 2021.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the approach evaluate changed vs unchanged urban areas?",{"text":84,"@type":76},"It carries out supervised classification and change detection on the region of interest, producing results that distinguish changed and unchanged urban areas over the selected period.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":29,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]