Investigadores tGIS colaboran con la UPM en la revisión de literatura sobre el uso de la IA en estudios relacionados con usos de suelos urbanos

4 jun 2025 - 15:17 CET

Autores: Haithem Drici, y José Carpio-Pinedo.

Resumen / Abstract:

This paper provides a comprehensive systematic review of Artificial Intelligence (AI) applications in urban land use mix at the granular level, a critical aspect of urban planning and sustainability. After screening 654 documents published between 2014 and 2024, 66 relevant studies are analyzed in detail. AI technologies are scrutinized for their potential to refine land use mix assessments and enhance the accuracy of urban functional planning tasks. Which could improve urban sustainability and foster spatial synergy by adeptly navigating the complexities of managing land use mix with AI-driven solutions. The review assesses these studies through three core dimensions: (1) AI techniques for urban land use classification and spatial interaction analysis, (2) AI-driven enhancement and optimization strategies for sustainable mixed-use development and management, and (3) AI tools enhancing participatory planning systems and decision-making processes. The review finds that, despite noteworthy progress and potential applicability, substantial challenges remain in fully integrating AI into the adaptive frameworks required by rapidly evolving urban contexts. The review identifies a diversity of research gaps that need to be addressed in future work, with the aim of refining AI techniques to better account for land use mix complexities and support more responsive socio-technical urban development initiatives.

Enlace: https://www.sciencedirect.com/science/article/pii/S0264275125004020

¿Cómo citarlo? Drici, H., & Carpio-Pinedo, J. (2025). Urban land use mix and AI: A systematic review. Cities, 165(10610), 2.