[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121876-en":3,"doc-seo-121876-105":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":4,"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},121876,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","COMPARISON OF GOOGLE EARTH ENGINE-BASED MACHINE LEARNING CLASSIFIERS FOR MAPPING AQUACULTURE PONDS IN SUNGAI UDANG, PENANG - Thesis","This master’s thesis compares Google Earth Engine (GEE)-based machine learning classifiers for mapping aquaculture ponds in Sungai Udang, Penang. The work reviews relevant literature on aquaculture, remote sensing, cloud computing, and classifier families such as CART, Random Forest, and Support Vector Machine. It defines a conceptual framework and study area, prepares Landsat and GEE imagery, performs image pre-processing and sample selection, then runs supervised image classification using the evaluated models. Results and discussion support selecting suitable classifiers for pond mapping in the study region.","COMPARISON OF GOOGLE EARTH ENGINEBASED MACHINE LEARNING CLASSIFIERS FOR MAPPING AQUACULTURE PONDS IN SUNGAI UDANG, PENANG  \nARVINTH A/L RAJANDRAN  \nUNIVERSITI SAINS MALAYSIA  \n2023  \nCOMPARISON OF GOOGLE EARTH ENGINEBASED MACHINE LEARNING CLASSIFIERS FOR MAPPING AQUACULTURE PONDS IN SUNGAI UDANG, PENANG  \nby  \nARVINTH A/L RAJANDRAN  \nThesis submitted in fulfilment of the requirements for the degree of  \nMaster of Arts  \nFebruary 2023  \nACKNOWLEDGEMENT  \nI would like to take this opportunity to express my sincere gratitude to my supervisor, Associate Professor GS. Dr. Tan Mou Leong for his support, guidance and encouragement. With his prompt and useful advice, he had helped me to reach this finishing point. Then, I would like to thank my co-supervisor, Dato ’ Professor Dr. Narimah Samat for her assistance and guidance. They have helped tremendously especially in improving my work.  \nIt is also my privilege to thank my colleague and friends, Zibeon bin Luhaim, Tew Yi Lin and Zeng Ju. They had given me their full support throughout this research and also a lifetime of unforgettable memories of their kindness, support and erudition. They kept showering me with motivation all the way to the end with their experiencesand useful knowledge.  \nThe research for this thesis was financially funded and supported by the Ministry of Higher Education of Malaysia under the Long-Term Research Grant Scheme (LRGS/1/2018/USM/01/1/5) (203/PHUMANITI/67215003) . Finally, thanks to all parties and Universiti Sains Malaysia for their facilities and funding for this research.  \nTABLE OF CONTENTS  \nACKNOWLEDGEMENT......................................................................................... ii  \nTABLE OF CONTENTS.......................................................................................... iii  \n[LIST OF TABLES .................................................................................................... vi](LIST OF TABLES .................................................................................................... vi)  \n[LIST OF FIGURES ................................................................................................ viii](LIST OF FIGURES ................................................................................................ viii)  \n[LIST OF ABBREVIATIONS ...............................................................](LIST OF ABBREVIATIONS ...............................................................).................... x  \nLIST OF APPENDICES ......................................................................................... xii  \nABSTRAK ............................................................................................................... xiii  \nABSTRACT .............................................................................................................. xv  \nCHAPTER 1 INTRODUCTION ........................................................................... 1  \n1.1 Motivation and Background ............................................................................. 1  \n1.2 Problem Statement ........................................................................................... 5  \n1.3 Objectives ......................................................................................................... 5  \n1.4 Scope of Research ............................................................................................ 6  \n1.5 Thesis Outline .................................................................................................. 6  \nCHAPTER 2 LITERATURE REVIEW................................................................ 8  \n2.1 Aquaculture ...................................................................................................... 8  \n2.2 Remote Sensing .............................................................................................. 12  \n2.2.1 Remote Sensing in Aquaculture Mapping ..................................... 13  \n2.2.","cbCaiiXWfjTou4h7","https://ap.wps.com/l/cbCaiiXWfjTou4h7","pdf",563442,1,41,"English","en",105,"# Acknowledgement\n# Table of Contents\n# List of Tables\n# List of Figures\n# List of Abbreviations\n# List of Appendices\n# Abstract\n# Chapter 1 Introduction\n## Motivation and Background\n## Problem Statement\n## Objectives\n## Scope of Research\n## Thesis Outline\n# Chapter 2 Literature Review\n## Aquaculture\n## Remote Sensing\n## Cloud Computing\n## Machine Learning Classifiers\n## Aquaculture Pond Mapping using Google Earth Engine\n## Research Gaps\n# Chapter 3 Methodology\n## Conceptual Framework\n## Study Area\n## Landsat Satellite Data\n## Google Earth Pro Imagery\n## GEE Cloud Computing Platform\n## Image Pre-processing\n## Selection of Training and Testing Sample\n## Image Classification\n## Random Forest (RF)","[{\"question\":\"What classifiers are compared in the thesis?\",\"answer\":\"The literature review and methodology cover Classification and Regression Tree (CART), Random Forest, and Support Vector Machine, followed by their comparison for pond mapping.\"},{\"question\":\"Which study area and data sources are used?\",\"answer\":\"The study targets aquaculture ponds in Sungai Udang, Penang and uses Landsat satellite data plus Google Earth Pro imagery within the Google Earth Engine cloud computing workflow.\"},{\"question\":\"What are the main steps in the Google Earth Engine methodology?\",\"answer\":\"The workflow includes image pre-processing, selection of training and testing samples, and supervised image classification using the chosen machine learning classifiers.\"}]","COMPARISON OF GOOGLE EARTH ENGINE-BASED MACHINE LEARNING CLASSIFIERS FOR MAPPING AQUACULTURE PONDS IN SUNGAI UDANG, PENANG - Thesis | PDF",1785807389,103,{"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},"comparison-of-google-earth-engine-based-machine-learning-classifiers-for-mapping-aquaculture-ponds-in-sungai-udang-penang-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@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":53},"https://docshare.wps.com/document/comparison-of-google-earth-engine-based-machine-learning-classifiers-for-mapping-aquaculture-ponds-in-sungai-udang-penang-thesis/121876/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What classifiers are compared in the thesis?","Question",{"text":75,"@type":76},"The literature review and methodology cover Classification and Regression Tree (CART), Random Forest, and Support Vector Machine, followed by their comparison for pond mapping.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which study area and data sources are used?",{"text":80,"@type":76},"The study targets aquaculture ponds in Sungai Udang, Penang and uses Landsat satellite data plus Google Earth Pro imagery within the Google Earth Engine cloud computing workflow.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main steps in the Google Earth Engine methodology?",{"text":84,"@type":76},"The workflow includes image pre-processing, selection of training and testing samples, and supervised image classification using the chosen machine learning classifiers.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"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":53,"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":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]