[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120208-en":3,"doc-seo-120208-105":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},120208,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Domain-Specific Machine Learning Approaches for Geospatial Problems - Dissertation","This dissertation develops novel algorithms for complex geospatial problems at the intersection of environmental, social, and computational sciences. It targets domain-specific challenges, especially the deviation from the independent and identical distribution (IID) assumption, and evaluates specialized methods across multiple settings. Contributions include environmental modeling under severe class imbalance for groundwater quality assessment using ANN, SVM, and extreme gradient boosting (XGB) with class-imbalance strategies. The work also improves high-resolution prediction of social unrest drivers via context-dependent and context-independent modeling with uncertainty quantification and feature importance analysis. Finally, it proposes a two-stage deep learning framework for geo-localizing non-georeferenced remotely-sensed imagery using contrastive learning, regression, and a specialized loss to raise localization precision.","University of Nebraska-Lincoln  \nDigitalCommons@University of Nebraska-Lincoln  \n\n| Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023– | Graduate Studies |\n| --- | --- |\n| 12-2024\u003Cbr>Domain-Specific Machine Learning Approaches for Geospatial Problems\u003Cbr>Shine Bedi\u003Cbr>University of Nebraska-Lincoln\u003Cbr>Follow this and additional works at: [https://digitalcommons.unl.edu/dissunl](https://digitalcommons.unl.edu/dissunl)\u003Cbr> Part of the Computer Sciences Commons |  |\n\nRecommended Citation  \nBedi, Shine, \"Domain-Specific Machine Learning Approaches for Geospatial Problems\" (2024) . Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023–. 246.  \n[https://digitalcommons.unl.edu/dissunl/246](https://digitalcommons.unl.edu/dissunl/246)  \nThis Dissertation is brought to you for free and open access by the Graduate Studies at  \nDigitalCommons@University of Nebraska-Lincoln. It has been accepted for inclusion in Dissertations and Doctoral Documents from University of Nebraska-Lincoln, 2023– by an authorized administrator of  \nDigitalCommons@University of Nebraska-Lincoln.  \nDOMAIN-SPECIFIC MACHINE LEARNING APPROACHES FOR GEOSPATIAL PROBLEMS  \nby  \nShine Bedi  \nA DISSERTATION  \nPresented to the Faculty of  \nThe Graduate College at the University of Nebraska In Partial Fulfilment of Requirements For the Degree of Doctor of Philosophy  \nMajor: Computer Science & Engineering  \nUnder the Supervision of Professors Ashok Samal and Stephen Scott  \nLincoln, Nebraska  \nDecember, 2024  \nDOMAIN-SPECIFIC MACHINE LEARNING APPROACHES FOR  \nGEOSPATIAL PROBLEMS  \nShine Bedi, Ph.D.  \nUniversity of Nebraska, 2024  \nAdvisors: Ashok Samal and Stephen Scott  \nThis dissertation explores novel algorithms for complex geospatial problems atthe intersection of environmental, social, and computational sciences. Emphasizing the unique challenges of the geospatial domain, particularly the deviation from the independent and identical distribution (IID) assumption, the research spans various methodologies across different domains, demonstrating the benefits of specialized approaches in spatial analysis.  \nFirst, we show that machine learning techniques can be effectively used in environmental modeling, which often has severe class imbalance challenges. Using artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB) and techniques to address class imbalance provides insights into groundwater quality assessment, focusing on pesticide and nitrate contamination.  \nSecond, our research advances the prediction of complex social phenomena at high spatial resolutions and assesses the impact of geographic context using predictive performance. The development of both context-dependent and context-independent approaches, augmented with uncertainty quantification and feature importance analysis, enables a better understanding of social unrest drivers while offering reliable predictions.  \nThird, our work addresses the fundamental challenge of geo-localization of nongeoreferenced remotely-sensed imagery (NRSI) through an innovative two-stage deep  \nlearning framework. The integration of contrastive learning and regression, coupled with a specialized loss function, enables geographically-aware modeling that significantly improves geo-localization precision over alternate approaches.  \nThese advances in environmental science, social science, and geospatial artificial intelligence demonstrate that specialized machine learning approaches can effectively address complex geospatial problems.  \niv  \nACKNOWLEDGMENTS  \nThis dissertation would not have been possible without my advisor, Dr. Samal, and co-advisor, Dr. Scott. They have been my guides, mentors, and supporters throughout my time in the Ph.D. program. Their willingness to share their knowledge and expertise helped me grow as a researcher, and their patience during the challenging phases of this work made all the difference.  \nI would like","cbCaig8gIXRSAN4m","https://ap.wps.com/l/cbCaig8gIXRSAN4m","pdf",30539290,1,146,"English","en",105,"# Introduction\n## Environmental Modeling\n## Social Process Modeling\n## Geo-localization of Non-Georeferenced Remotely-Sensed Imagery\n## Research Contributions\n## Dissertation Outline\n# Comparative evaluation of machine learning models for groundwater quality assessment\n## Abstract\n## Introduction\n## Background\n## Materials and methods","[{\"question\":\"What problem does the dissertation focus on?\",\"answer\":\"It focuses on developing machine learning methods for complex geospatial problems spanning environmental, social, and computational sciences.\"},{\"question\":\"How does the research address environmental modeling challenges?\",\"answer\":\"It targets severe class imbalance in groundwater quality assessment using ANN, SVM, and extreme gradient boosting (XGB) combined with class-imbalance handling techniques.\"},{\"question\":\"What method is proposed for geo-localization of non-georeferenced remotely-sensed imagery?\",\"answer\":\"It introduces a two-stage deep learning framework that combines contrastive learning and regression with a specialized loss function to improve geo-localization precision.\"}]","Domain-Specific Machine Learning Approaches for Geospatial Problems - 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