GeoAI tools for urban spaces and their built heritage

As happened in many disciplines, also in the Geomatic field, the use of Artificial Intelligence (AI) methods such as Machine Learning (ML) and Deep Learning (DL) had a rapid and pioneering development, substantially overturning the entire science of geoinformation. These innovations contribute to optimising consolidated methods, opening new research avenues and enabling new solutions, among which those relating to urban environments and their current needs in relation to the continuous transformations they undergo will be addressed and discussed. However, this progress also increasingly raises significant challenges, including ethical considerations and questions on AI implementation. The present contribution analyses the relevant operational advancements in the context of urban space and its building heritage, highlighting the research directions that influence the development of the tools for better urban planning control and addressing challenges affecting its evolution. A complex set of research directions emerges in the realm of 3D city models, which aim to integrate cognitive systems that allow the combination of geometric and semantic information. These efforts focus on making model creation more time-efficient and sustainable in terms of effectiveness, usability and application potential. Classification and subsequent segmentation of image and range-based products, such as point clouds, DTMs, and other photogrammetric data, are extensively investigated using both ML and DL methods at different scales, ranging from the urban to the architectural scale. Both levels of analysis are crucial components of three-dimensional urban databases, and the paper will present a study focused on and supporting the urban and architectural scale, developed in the framework of the Geomatic Lab of Politecnico di Torino activities.