Thesis Abstract

Abstract
Urban land use classification is a crucial aspect of urban planning and development, as it provides valuable insights into the spatial distribution of different land uses within a city. This thesis focuses on utilizing remote sensing and Geographic Information System (GIS) techniques to classify urban land use, with the aim of enhancing the accuracy and efficiency of land use mapping. The study area selected for this research is a rapidly growing urban region facing challenges related to urban sprawl, land use change, and environmental degradation. The research begins with a comprehensive review of existing literature on urban land use classification, remote sensing technologies, GIS applications, and methodologies for land use mapping. The literature review highlights the importance of accurate land use classification for informed decision-making in urban planning and sustainable development. The methodology chapter outlines the research design, data collection methods, and analysis techniques employed in this study. Remote sensing data, including satellite imagery and aerial photographs, are processed using image classification algorithms to extract land cover information. GIS software is utilized to integrate and analyze the classified land use data, generating thematic maps that depict different land use categories within the study area. The results chapter presents the findings of the land use classification process, including the identification and mapping of various land use types such as residential areas, commercial zones, industrial estates, green spaces, and transportation networks. Accuracy assessment techniques are applied to evaluate the performance of the classification model and validate the accuracy of the land use maps generated. The discussion chapter provides a detailed interpretation and analysis of the research findings, discussing the implications of the classified land use information for urban planning and management. The study identifies patterns of land use distribution, spatial relationships between different land use categories, and potential areas of land use conflict or synergy within the urban landscape. The conclusion chapter summarizes the key findings of the research, highlighting the contributions of remote sensing and GIS techniques to urban land use classification. The study underscores the importance of accurate land use mapping for effective urban planning strategies, resource management, and sustainable development initiatives. Recommendations for future research and practical applications of the study findings are also provided. Overall, this thesis contributes to the advancement of knowledge in urban land use classification using remote sensing and GIS technologies, offering valuable insights and methodologies for enhancing spatial analysis and decision-making in urban planning contexts.

Thesis Overview

The project titled "Urban Land Use Classification Using Remote Sensing and GIS Techniques" aims to explore and implement advanced technologies in the field of surveying and geo-informatics to accurately classify urban land use patterns. With the rapid urbanization and expansion of cities globally, the ability to effectively monitor and analyze land use is crucial for urban planning, resource management, and sustainable development. This research project focuses on leveraging remote sensing data and Geographic Information System (GIS) techniques to classify urban land use, which involves categorizing different land cover types within urban areas. By using satellite imagery and GIS tools, the project seeks to develop a systematic approach to classify land use patterns such as residential areas, commercial zones, industrial sites, green spaces, and transportation networks. The project will begin with a comprehensive literature review to explore existing studies, methodologies, and technologies related to urban land use classification using remote sensing and GIS. This review will provide a solid foundation for understanding the current state-of-the-art techniques and identify gaps in the literature that the project aims to address. The research methodology will involve data collection, processing, and analysis of remote sensing data obtained from satellites or aerial platforms. Various image processing techniques, such as image classification algorithms and feature extraction methods, will be employed to extract meaningful information from the satellite imagery. GIS tools will be utilized to integrate and analyze the classified land use data within the spatial context of urban areas. The findings of this project are expected to contribute to the field of surveying and geo-informatics by providing insights into the application of remote sensing and GIS techniques for urban land use classification. By accurately mapping and classifying urban land use patterns, this research can support urban planners, policymakers, and decision-makers in making informed decisions regarding land use management, infrastructure development, and environmental conservation. Overall, "Urban Land Use Classification Using Remote Sensing and GIS Techniques" aims to showcase the potential of advanced technologies in transforming the way urban land use is classified and analyzed. This research project holds significance in enhancing our understanding of urban landscapes and supporting sustainable urban development practices for the benefit of society and the environment.