Innovative AI Approaches to Enhance Land Use Prediction and Management

In the context of rapid urbanization and growing environmental challenges, land use prediction has become a critical area of study for sustainable development and urban planning. Traditional methods often struggle to handle the complexity and dynamic nature of land use changes. This article explores innovative approaches to enhance land use prediction and management using Artificial Intelligence (AI). It discusses various AI techniques, including machine learning models, deep learning approaches, reinforcement learning, Geospatial AI (GeoAI), hybrid models, and spatial-temporal models, highlighting their potential to improve prediction accuracy. The applications and benefits of AI-driven land use prediction are examined, demonstrating its impact on urban planning, agricultural optimization, environmental conservation, and disaster management. Despite the significant advancements, challenges such as data quality, computational requirements, model interpretability, and integration with policy are identified. Future directions for research and implementation are also discussed, emphasizing the need for high-quality data, improved model interpretability, and effective collaboration between researchers, policymakers, and stakeholders. The article concludes that AI offers promising solutions for enhancing land use prediction and management, contributing to more sustainable and efficient land resource utilization.