Thesis Abstract

Abstract
The rapid urbanization and development of cities worldwide have heightened the demand for accurate and efficient methods of monitoring and classifying land cover in urban areas. This thesis explores the integration of LiDAR (Light Detection and Ranging) and UAV (Unmanned Aerial Vehicle) data to enhance the precision and reliability of land cover classification in urban environments. The primary objective of this study is to investigate the synergistic potential of LiDAR and UAV technologies in capturing high-resolution data for improved land cover classification accuracy. The introductory chapter sets the stage by providing a comprehensive overview of the research topic. It delves into the background of the study, highlighting the significance of accurate land cover classification in urban planning and environmental management. The problem statement underscores the existing challenges and limitations in conventional land cover classification methods, paving the way for the exploration of innovative approaches using LiDAR and UAV data. The objectives of the study are outlined to guide the research process, while the scope and limitations help define the boundaries within which the research operates. The chapter concludes with a discussion on the significance of the study and an overview of the thesis structure. Chapter two presents a detailed literature review encompassing ten key themes related to LiDAR, UAV technology, land cover classification, and their applications in urban areas. The review synthesizes existing knowledge, identifies gaps in the literature, and provides a theoretical foundation for the research. Chapter three focuses on the research methodology employed to achieve the study objectives. It covers eight essential components, including data acquisition, preprocessing, feature extraction, classification algorithms, accuracy assessment, and validation techniques. The chapter outlines the step-by-step process followed in integrating LiDAR and UAV data for land cover classification, emphasizing the methodological rigor and analytical techniques employed. Chapter four presents an elaborate discussion of the research findings, highlighting the effectiveness of the integrated LiDAR and UAV approach in achieving accurate land cover classification in urban areas. The chapter examines the classification results, assesses the accuracy of the classification model, and discusses the implications of the findings for urban planning and environmental management. Finally, chapter five offers a comprehensive conclusion and summary of the thesis. It synthesizes the key findings, discusses the research implications, and proposes recommendations for future studies. The conclusion reinforces the significance of integrating LiDAR and UAV data for accurate land cover classification in urban areas and underscores the potential impact of this research on urban planning practices. In summary, this thesis contributes to the advancement of land cover classification methodologies in urban areas by demonstrating the synergistic potential of LiDAR and UAV technologies. The integration of these data sources offers a promising approach for enhancing the accuracy and efficiency of land cover mapping, thereby supporting informed decision-making in urban development and environmental planning.

Thesis Overview