[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124737-en":3,"doc-seo-124737-105":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},124737,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Remote Sensing of Mangroves using Machine Learning based on Satellite and Aerial Imagery - Thesis","A master’s thesis in Electrical Engineering (Machine Learning & Data Science) presenting an end-to-end approach for mapping mangroves by fusing satellite and aerial imagery with machine learning. The work reviews remote sensing challenges across low- and high-resolution data, introduces classical models and deep learning with convolutional neural networks, and details a complete data pipeline including image processing, drone/satellite workflows, and image labeling. Field expeditions support model training and validation, and experimental results evaluate a hybrid architecture for improved mangrove detection.","UC San Diego  \nUC San Diego Electronic Theses and Dissertations  \nTitle  \nRemote Sensing of Mangroves using Machine Learning based on Satellite and Aerial Imagery  \nPermalink  \n[https://escholarship.org/uc/item/4pf2f7tr](https://escholarship.org/uc/item/4pf2f7tr)  \nAuthor  \nHicks, Stanley Dillon  \nPublication Date  \n2023  \nPeer reviewed|Thesis/dissertation  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA SAN DIEGO  \nRemote Sensing of Mangroves using Machine Learning based on Satellite and Aerial Imagery  \nA Thesis submitted in partial satisfaction of the requirements for the degree Master of Science  \nin  \nElectrical Engineering (Machine Learning & Data Science)  \nby  \nStanley Dillon Hicks  \nCommittee in charge:  \nProfessor Curt Schurgers, Chair  \nProfessor Ryan Kastner  \nProfessor Karcher Morris  \nCopyright  \nStanley Dillon Hicks, 2023 All rights reserved.  \nThe Thesis of Stanley Dillon Hicks is approved, and it is acceptable in quality and form for publication on microfilm and electronically.  \nUniversity of California San Diego  \n2023  \nDEDICATION  \nTo my family and friends who have always supported me, And to my father and sister who were able to see my journey, but not the destination  \nTABLE OF CONTENTS  \nTHESIS APPROVAL PAGE ........................................................................................................ iii  \nDEDICATION ............................................................................................................................... iv  \nTABLE OF CONTENTS .................................................................................................................v  \nLIST OF FIGURES...................................................................................................................... viii  \nLIST OF TABLES ...........................................................................................................................x  \nLIST OF ABBREVIATIONS ........................................................................................................ xi  \nACKNOWLEDGEMENTS .......................................................................................................... xii  \nABSTRACT OF THE THESIS .................................................................................................... xiii  \n1. Introduction .......................................................................................................................... 1  \n1.1. Background .............................................................................................................. 1  \n1.2. Mangrove Remote Sensing ......................................................................................2  \n1.2.1. Low-Resolution-Satellites..................................................................................2  \n1.2.2. High-Resolution-Drones ....................................................................................5  \n1.2.3. Machine Learning ................................................................................................8  \n2. Background: Machine Learning ........................................................................................10  \n2.1. Machine Learning: Background ............................................................................10  \n2.1.1. Decision Trees ................................................................................................... 11  \n2.1.2. Bias, Variance, and Ensembling ........................................................................12  \n2.1.3. Implementing Classical Models.........................................................................14  \n2.2. Deep Learning and Convolutional Neural Networks.............................................15  \n2.2.1. Artificial Neural Networks ................................................................................15  \n2.2.2. Convolutional Neura","cbCaihPMUSdlwzuZ","https://ap.wps.com/l/cbCaihPMUSdlwzuZ","pdf",4460018,1,74,"English","en",105,"# Introduction\n## Background\n## Mangrove Remote Sensing\n## Low-Resolution Satellites\n## High-Resolution Drones\n## Machine Learning\n# Background: Machine Learning\n## Decision Trees\n## Bias, Variance, and Ensembling\n## Implementing Classical Models\n## Deep Learning and Convolutional Neural Networks\n## EfficientNet\n# Machine Learning: Application\n## Low-Resolution Imagery\n## High-Resolution Imagery\n## High- and Low-Resolution Imagery\n## Hybrid Model Architecture\n# Expeditions\n## Mexico Expedition\n## Jamaica Expedition\n# Data Pipeline\n## Image Processing\n## Drone Imagery\n## Satellite Imagery\n## Image Labeling\n# Results","[{\"question\":\"What data sources are used for mangrove mapping?\",\"answer\":\"The thesis uses both satellite imagery (low resolution) and aerial imagery from drones (high resolution), and also studies how combining both affects performance.\"},{\"question\":\"Which machine learning methods are covered?\",\"answer\":\"It reviews classical machine learning including decision trees and discusses deep learning using convolutional neural networks, including EfficientNet.\"},{\"question\":\"How is the project organized from data to results?\",\"answer\":\"It defines a data pipeline covering image processing for drone and satellite imagery, image labeling, and a hybrid model architecture, followed by results evaluation supported by expeditions.\"}]","Remote Sensing of Mangroves using Machine Learning based on Satellite and Aerial Imagery - 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