Project Abstract

The rapid urbanization and growth of cities worldwide have led to significant changes in land use patterns, posing challenges for sustainable urban planning and decision-making. This project aims to develop a comprehensive spatial modeling framework to analyze and predict urban land use change using remote sensing data and geographic information systems (GIS) techniques. The importance of this project lies in its ability to provide valuable insights into the dynamics of urban land use, enabling urban planners and policymakers to make informed decisions for sustainable development. By integrating remote sensing data and GIS, this project will create a robust platform for monitoring, analyzing, and forecasting urban land use changes, which are crucial for managing the complex and ever-evolving urban environments. The project begins by collecting and preprocessing multitemporal satellite imagery and other geospatial data, such as land cover maps, census data, and transportation networks. These data will be used to identify and map the historical changes in urban land use patterns over a defined time period. The project will employ advanced image classification techniques, including supervised and unsupervised methods, to accurately delineate the various land use and land cover categories within the study area. The next step involves the development of a spatial modeling framework to analyze the drivers and patterns of urban land use change. This framework will incorporate a range of factors, such as population growth, economic activities, transportation infrastructure, and environmental conditions, to understand the complex interplay between these variables and their influence on urban land use dynamics. Commonly used spatial modeling approaches, such as Cellular Automata (CA) and Markov Chain models, will be integrated into the framework to forecast future land use changes based on the identified patterns and trends. To enhance the predictive capabilities of the model, the project will also explore the integration of machine learning algorithms, which have the potential to capture the nonlinear relationships and complex interactions inherent in urban land use systems. These advanced techniques will be leveraged to improve the accuracy and reliability of the land use change projections, enabling more effective urban planning and decision-making. The project will further incorporate scenario-based analyses to assess the potential impacts of various policy interventions, land use management strategies, and environmental factors on the future urban landscape. This will provide urban planners and policymakers with a valuable decision-support tool for evaluating the consequences of different development scenarios and formulating appropriate strategies for sustainable urban growth. The expected outcomes of this project include a comprehensive spatial modeling framework for urban land use change analysis, a set of high-resolution land use maps depicting historical and future trends, and a decision-support system that can aid in the development of sustainable urban planning policies. The findings from this project will contribute to the growing body of knowledge in the field of urban geography, remote sensing, and GIS, and will have practical implications for urban planners, environmental managers, and policymakers worldwide.

Project Overview