Smart Land Use Planning: Integrating AI, GIS, and Remote Sensing for Sustainable Development

The exponential rise in urbanization, deforestation, and unsustainable agricultural expansion has highlighted the urgent need for data-driven, adaptive, and environmentally conscious land use planning strategies. Traditional approaches to land use planning often suffer from limited data integration, spatial analysis, and scenario modeling capabilities. This proposed chapter aims to present a comprehensive framework for smart land use planning by harnessing the synergistic potential of Artificial Intelligence (AI), Geographic Information Systems (GIS), and Remote Sensing (RS) technologies. The chapter will begin by reviewing historical and contemporary land use planning methodologies, followed by a detailed assessment of the current environmental and socio-economic challenges associated with land degradation, habitat loss, and climate change. We will then explore the capabilities of remote sensing in mapping land cover changes and GIS in spatial data modeling. Particular emphasis will be placed on how AI algorithms—especially machine learning (ML) and deep learning (DL)—enhance predictive modeling, land suitability analysis, and decision support systems for sustainable land use management. Case studies from different geographies will be discussed to illustrate practical applications, including AI-driven land suitability mapping for agriculture, forest conservation planning, urban sprawl monitoring, and climate-resilient infrastructure development. The chapter will conclude with a discussion on policy implications, ethical considerations, and future trends in digital land use governance. By integrating technological innovations with sustainability goals, this chapter will provide both theoretical insights and practical tools for land use planners, environmental policymakers, and researchers aiming to foster a resilient and sustainable future.