[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120195-en":3,"doc-seo-120195-105":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},120195,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Deforestation monitoring based on machine learning techniques - Master’s Thesis","Deforestation monitoring is addressed through a machine learning framework that leverages satellite observations and change detection concepts to classify forest dynamics. The work organizes a full pipeline from data sourcing and region-of-interest selection to ground-truth construction and database assembly. Three predictive models—Random Forest, XGBoost, and a multilayer perceptron—are trained and evaluated, with results compared via confusion matrices and learning curves. A budgeting and impact assessment section supports practical and responsible deployment considerations.","Deforestation monitoring based on machine  \nlearning techniques  \nMaster’s Thesis  \nsubmitted to the Faculty of the  \nEscola T`ecnica d’Enginyeria de Telecomunicaci´o de Barcelona Universitat Polit`ecnica de Catalunya by  \nMar´ıa Isabel Manresa Rom´an  \nIn partial fulfillment  \nof the requirements for the Master’s degree in Advanced Telecommunication Technologies  \nAdvisors: Javier Ruiz Hidalgo, Carlos L´opez Mart´ınez, Teresa Dom`enech Abell´o  \nBarcelona, 17/05/2024  \nContents  \nList of Figures 4  \nList of Tables 4  \n1 Introduction 10  \n1.1 Project Overview and Statement of purpose ................. 10  \n1.2 Objectives .................................... 11  \n1.3 Workplan .................................... 11  \n2 State of the Art and Fundamentals 14  \n2.1 Forest Types .................................. 14  \n2.2 Satellite Data .................................. 15  \n2.2.1 Sentinel-2 ................................ 15  \n2.3 Models ...................................... 17  \n2.3.1 Random Forest ............................. 17  \n2.3.2 XGBoost ................................. 18  \n2.3.3 MLP ................................... 19  \n2.4 Change Detection ................................ 20  \n3 Methodology 23  \n3.1 Database ..................................... 23  \n3.1.1 Ground Truth .............................. 23  \n3.1.2 Regions of Interest ........................... 25  \n3.1.3 Database Construction ......................... 27  \n3.2 Models ...................................... 29  \n3.2.1 Random Forest ............................. 30  \n3.2.2 XGBoost ................................. 32  \n3.2.3 MLP ................................... 32  \n4 Results 36  \n4.1 Database ..................................... 36  \n4.2 Random Forest ................................. 40  \n4.3 XGBoost ..................................... 41  \n4.4 MLP ....................................... 42  \n4.5 Summary .................................... 46  \n5 Budget 48  \n5.1 Material ..................................... 48  \n5.2 Salaries ..................................... 48  \n5.3 Total Costs ................................... 48  \n6 Environmental Impact 50  \n6.1 Environmental Impact ............................. 50  \n6.2 Economic Impact ................................ 50  \n6.3 Social Impact .................................. 50  \n7 Conclusions 52  \n8 Future Work 52  \nReferences 54  \nAppendices 56  \nA Work Plan 56  \nList of Figures  \n1 Input and output scheme ............................ 10  \n2 Gantt (1) .................................... 11  \n3 Gantt (2) .................................... 12  \n4 Gantt (3) .................................... 12  \n5 Gantt (4) .................................... 12  \n6 Gantt (5) .................................... 12  \n7 Gantt (6) .................................... 13  \n8 Forest types ................................... 14  \n9 Architecture of a random forest model..................... 18  \n10 Forest Cover Change map ........................... 21  \n11 Spatial and temporal availability of cloud free optical observations ..... 21  \n12 Methodology workflow ............................. 23  \n13 Tasks definition and classes .......................... 25  \n14 Workflow of the ground truth ......................... 25  \n15 Data timeline .................................. 26  \n16 Database construction workflow ........................ 27  \n17 Incomplete Sentinel-2 Image .......................... 27  \n18 Example of the georeferenciation of a band .................. 28  \n19 Final MLP architecture ............................. 34  \n20 Regions of interest ............................... 36  \n21 Justification of the discard of Bolivia ..................... 37  \n22 FNF maps of the regions of interest ...................... 38  \n23 Unbalanced histograms ............................. 39  \n24 Learning curves best MLP ........................... 44  \n25 Learning curves high capacity MLP ...................... 45  \nListings  \nL","cbCaiqytxLZJenc9","https://ap.wps.com/l/cbCaiqytxLZJenc9","pdf",15768888,1,60,"English","en",105,"# Introduction\n## Project Overview and Statement of purpose\n## Objectives\n## Workplan\n# State of the Art and Fundamentals\n## Forest Types\n## Satellite Data\n## Models\n## Change Detection\n# Methodology\n## Database\n## Models\n# Results\n## Database\n## Random Forest\n## XGBoost\n## MLP\n## Summary\n# Budget\n# Environmental Impact\n# Conclusions\n# Future Work\n# References\n# Appendices","[{\"question\":\"Which satellite data source is used for deforestation monitoring?\",\"answer\":\"The methodology builds on Sentinel-2 data, including discussion of Level-1C and Level-2A products and cloud-free optical observations.\"},{\"question\":\"What machine learning models are evaluated in the study?\",\"answer\":\"Random Forest, XGBoost, and a multilayer perceptron (MLP) are implemented and compared for change classification tasks.\"},{\"question\":\"How are the models evaluated and how are results summarized?\",\"answer\":\"Evaluation relies on confusion matrices and inference test results, with learning curves used to analyze training behavior; summaries compare performance across tasks and models.\"}]","Deforestation monitoring based on machine learning techniques - Master’s Thesis | 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satellite data source is used for deforestation monitoring?","Question",{"text":75,"@type":76},"The methodology builds on Sentinel-2 data, including discussion of Level-1C and Level-2A products and cloud-free optical observations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning models are evaluated in the study?",{"text":80,"@type":76},"Random Forest, XGBoost, and a multilayer perceptron (MLP) are implemented and compared for change classification tasks.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the models evaluated and how are results summarized?",{"text":84,"@type":76},"Evaluation relies on confusion matrices and inference test results, with learning curves used to analyze training behavior; summaries compare performance across tasks and 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