Advancing Land-Use Dynamics Modeling for Urban Landscape Management: An Artificial Intelligence Approach

Abstract

Motives: Urban landscapes around the world are confronting unprecedented challenges due to rapid urbanization. These challenges include urban sprawl, the degradation of green spaces, and escalating pollution levels, all of which threaten the sustainability and livability of urban environments. Integrating Artificial Intelligence (AI) into urban planning and management processes emerges as a promising solution to these complex issues, offering a new perspective on modeling land-use dynamics informed by diverse urban contexts.

Aim: This research endeavors to develop and refine predictive models for urban landscape management through the application of AI technologies. It specifically aims to analyze three recent studies focusing on Beijing, China; Santiago Metropolitan Area, Chile; and Wuhan, China. By harnessing the capabilities of machine learning and deep learning algorithms, the study seeks to enhance the accuracy, efficiency, and comprehensiveness of land-use dynamics modeling, thereby providing urban planners and policymakers with advanced tools for informed decision-making.

Results: The application of AI techniques in land-use dynamics modeling, as informed by the analyses of the three studies, has led to significant advancements in predicting and managing urban sprawl, land cover changes, and their associated environmental impacts. These results emphasize the improved capabilities of AI-driven models to enhance decision-making processes for urban planners and policymakers, contributing to the sustainable development of urban landscapes. Despite challenges such as data scarcity and the need for model adaptability across different urban contexts, the research underlines the transformative potential of AI in reshaping urban landscape management practices, based on insights drawn from the specific case studies of Beijing, Santiago, and Wuhan.