AI-Driven Data Integration in Real Estate Development Processes

The fragmentation of data relating to the built environment represents a critical obstacle to urban management. Although there are numerous sources, from satellite images to environmental sensors to socio-demographic and cadastral datasets, these resources often lack interoperability and integration, limiting the ability of administrations and stakeholders to carry out accurate monitoring, define informed real estate development strategies, and promote sustainable regeneration practices. This research explores how the integration of advanced Artificial Intelligence (AI) models, in particular Generative Adversarial Networks (GANs), can contribute to the development of innovative approaches to urban management and the enhancement of abandoned building stock. The analysis investigates the role of AI in Due Diligence (DD) processes through a review of the literature in the areas of smart cities (SC) and urban management (UM), to outline a theoretical and methodological framework to support data-driven urban regeneration strategies. These technologies combine heterogeneous inputs, such as geospatial structures, land use data, environmental performance and socio-economic indicators, to generate predictive urban models. The research adopts a multidimensional approach based on a systematic literature review, which identified more than 1,200 academic contributions. The most relevant analyses have been grouped into two main areas: “Artificial Intelligence and Smart Cities” and “Artificial Intelligence and Urban Management,” with a particular focus on (DD) and models for enhancing architectural heritage. Integrating AI capabilities into urban regeneration strategies can foster resilient and smart cities that can optimise resources by enhancing technical feasibility and economic sustainability through rigorous due diligence and risk assessment.