Thesis Abstract

Abstract
The integration of Light Detection and Ranging (LiDAR) and Unmanned Aerial Vehicle (UAV) technologies has revolutionized land cover classification in urban areas. This thesis explores the utilization of LiDAR and UAV technology to enhance the accuracy and efficiency of land cover classification processes in urban environments. The study focuses on the benefits and challenges associated with integrating these advanced technologies to improve the classification of land cover types such as buildings, vegetation, roads, and water bodies. The research begins with a comprehensive literature review that examines previous studies on LiDAR, UAV technology, and land cover classification methods. The review highlights the strengths and limitations of existing techniques and sets the foundation for the proposed integration approach. The research methodology section outlines the specific steps involved in collecting LiDAR and UAV data, preprocessing the data, and applying classification algorithms to produce accurate land cover maps. Various classification algorithms such as Random Forest, Support Vector Machine, and Neural Networks are compared to determine the most suitable method for urban land cover classification. The findings from the study reveal that the integration of LiDAR and UAV technology significantly improves the accuracy and efficiency of land cover classification in urban areas. The high-resolution data obtained from LiDAR and UAV sensors enable the identification of complex land cover features with greater precision. The study also identifies challenges such as data processing requirements, cost implications, and regulatory issues that need to be addressed when implementing these technologies. The discussion section delves into the implications of the research findings and provides insights into the practical applications of LiDAR and UAV technology for urban land cover classification. The potential for monitoring urban growth, environmental planning, disaster management, and infrastructure development is highlighted as key areas where this integrated approach can make a significant impact. In conclusion, this thesis demonstrates the effectiveness of integrating LiDAR and UAV technology for land cover classification in urban areas. The research contributes to the advancement of geospatial technology applications and provides valuable insights for researchers, practitioners, and policymakers involved in urban planning and environmental management. The study underscores the importance of harnessing the capabilities of LiDAR and UAV technology to address the complex challenges associated with urban land cover classification.

Thesis Overview

The project titled "Integration of LiDAR and UAV Technology for Land Cover Classification in Urban Areas" aims to explore the synergies between LiDAR (Light Detection and Ranging) and UAV (Unmanned Aerial Vehicle) technologies in the context of land cover classification within urban environments. This research seeks to address the growing need for accurate and efficient methods of mapping and monitoring land cover in urban areas, where rapid development and population growth are leading to significant changes in land use patterns. The integration of LiDAR and UAV technologies offers a promising approach to overcome the limitations of traditional methods of land cover classification. LiDAR technology provides high-resolution elevation data, while UAVs offer flexibility and accessibility for capturing high-resolution imagery. By combining these technologies, it is possible to generate detailed and up-to-date information on land cover types, such as buildings, roads, vegetation, and water bodies, within urban areas. The research will begin with a comprehensive review of existing literature on LiDAR, UAV technology, and land cover classification methods. This review will highlight the advantages and limitations of each technology and identify gaps in current research that can be addressed through the proposed integration approach. The methodology chapter will outline the specific steps involved in utilizing LiDAR and UAV technologies for land cover classification in urban areas. This will include details on data acquisition, processing techniques, feature extraction, and classification algorithms. The research will also consider the integration of ground truth data and validation methods to ensure the accuracy and reliability of the classification results. The discussion of findings chapter will present the results of the land cover classification process, including the accuracy assessment of the classification outputs. The research will explore the effectiveness of integrating LiDAR and UAV data for improving the classification accuracy and mapping capabilities in urban areas. The chapter will also discuss the implications of the findings for urban planning, environmental management, and disaster risk assessment. In conclusion, the project will summarize the key findings and contributions of the research, highlighting the potential benefits of integrating LiDAR and UAV technology for land cover classification in urban areas. The research will also identify areas for further investigation and recommend potential applications of the integrated approach in real-world scenarios. Overall, the project "Integration of LiDAR and UAV Technology for Land Cover Classification in Urban Areas" seeks to advance the field of surveying and geo-informatics by demonstrating the value of combining advanced technologies for more accurate and efficient land cover mapping in urban environments.