Thesis Abstract

Abstract
This thesis investigates the Integration of Remote Sensing and Geographic Information Systems (GIS) for Land Use Classification in Urban Areas. The rapid urbanization and expansion of cities worldwide have led to increased pressure on land resources, making accurate land use classification essential for urban planning and management. Remote Sensing data, such as satellite imagery, offers a valuable tool for capturing land cover information over large areas, while GIS provides the platform for data integration and analysis. This research aims to develop a methodology that combines Remote Sensing and GIS techniques to classify land use in urban areas accurately. The study begins with a comprehensive review of existing literature on Remote Sensing, GIS, land use classification, and their applications in urban areas. The literature review highlights the strengths and limitations of current methodologies and identifies gaps in research that this study aims to address. The research methodology section outlines the data collection process, image pre-processing techniques, feature extraction methods, and classification algorithms used to classify land use types in urban areas. The study area selected for this research is a rapidly urbanizing city in order to demonstrate the practical application of the proposed methodology. The findings of this research reveal the effectiveness of integrating Remote Sensing and GIS for land use classification in urban areas. The classification accuracy achieved through the proposed methodology is assessed using ground-truth data and validation techniques. The results demonstrate the potential of Remote Sensing and GIS to provide detailed land use information that can support urban planning decisions and monitoring of land cover changes over time. The discussion of findings section provides a detailed analysis of the classification results, highlighting the strengths and limitations of the methodology. The challenges encountered during the research process are discussed, along with recommendations for future research to improve the accuracy and efficiency of land use classification in urban areas. The conclusion summarizes the key findings of the study and emphasizes the significance of integrating Remote Sensing and GIS for effective land use classification in urban areas. In conclusion, this research contributes to the existing body of knowledge on land use classification in urban areas by proposing a methodology that leverages the capabilities of Remote Sensing and GIS technologies. The findings of this study have implications for urban planners, policymakers, and researchers involved in land use management and sustainable urban development. By enhancing the accuracy and efficiency of land use classification, this research aims to support informed decision-making for the sustainable development of urban areas.

Thesis Overview