The paper presents a predictive framework for monitoring architectural decay by integrating 3D survey data with artificial intelligence techniques. The methodology is structured as a Digital Decision System (DDS) that combines multi-source data acquisition (laser scanning, photogrammetry, mobile mapping, and low-cost sensors) with machine learning models to forecast degradation trends. Following a Knowledge Discovery in Database (KDD) pipeline—comprising data collection, preprocessing, transformation, data mining, and interpretation—the system uses linear regression to model the relationship between temporal data and measurable degradation phenomena. The approach is validated through experimental applications, demonstrating how predictive models can support conservation strategies, optimize monitoring processes, and assist decision-making in architectural restoration.
Dataspace: Predictive Survey as a Tool for a Data Driven Design for Public Space
This paper proposes a data-driven methodological framework for the analysis, simulation, and design of public space through predictive survey techniques. The research integrates digital survey methods, big data acquisition, and artificial intelligence to construct a three-dimensional model capable of supporting decision-making processes in urban design. The approach is structured into four phases: historical and contextual analysis, morphometric data acquisition through photogrammetry and LiDAR, digital model construction, and predictive simulation.
The study develops a digital twin environment in which heterogeneous data—collected from field surveys, low-cost sensors, and human-generated sources—are integrated and analyzed. As illustrated in the workflow diagram (Fig. 1, p. 705), the system combines physical and digital datasets to simulate future scenarios and evaluate public space quality. The methodology incorporates both quantitative metrics and qualitative assessments based on Gehl’s criteria, enabling the interpretation of spatial use, environmental conditions, and user behavior. The results demonstrate that predictive survey, combined with IoT and machine learning techniques, can support the design of responsive and adaptive urban environments, while highlighting current limitations in data acquisition, integration, and systematization.