Development of an Automated Land Use Classification System Using Remote Sensing Technology in Urban Areas

Thesis Abstract

Abstract
This thesis presents the development of an automated land use classification system utilizing remote sensing technology in urban areas. The rapid urbanization and expansion of cities have led to increased demand for accurate and efficient methods for monitoring and managing land use. Remote sensing technology offers a powerful tool for capturing and analyzing spatial data, making it ideal for land use classification applications. The aim of this study is to design and implement an automated system that can classify land use in urban areas using remote sensing data. The research begins with a comprehensive review of existing literature on remote sensing technology, land use classification methods, and applications in urban areas. The review identifies gaps in current research and provides a foundation for the development of the proposed system. Chapter three outlines the research methodology, including data collection, preprocessing, feature extraction, and classification algorithms. The methodology aims to ensure the accuracy and reliability of the classification system. The research findings are discussed in chapter four, which presents the results of the land use classification process in urban areas. The discussion includes an analysis of the accuracy of the classification system and comparisons with manual classification methods. The findings demonstrate the effectiveness of the automated system in accurately classifying land use in urban areas, highlighting its potential for improving urban planning and management. The significance of this study lies in its contribution to the field of land use classification and urban planning. The developed system offers a practical and efficient solution for monitoring land use changes in rapidly growing urban areas. The automated nature of the system reduces the time and resources required for classification, making it a valuable tool for urban planners, policymakers, and researchers. In conclusion, this thesis presents a novel approach to land use classification in urban areas using remote sensing technology. The developed automated system demonstrates high accuracy and efficiency in classifying land use, providing valuable insights for urban planning and management. The findings of this study have implications for sustainable urban development and offer a foundation for future research in the field of remote sensing and land use classification.