This paper presentation focuses on the adoption of big data visual representation and AI driven semantic interpretation to study complex urban patterns in places largely impervious to traditional mapping technologies and documentary analytical tools. Particularly the research focuses on industrial parks recently developed in Southeast Asia as part of a broader phenomenon of industrial delocalization. One could say that, in these countries, industrial parks are the DNA of contemporary urbanization. Industrial parks generate urban mobilities, attracting capital and labor as well as purchases and leisure, contribute to city’s expansions outside established boundaries, and provide viable infrastructure for the formation of new urban hubs in remote places. In many of these high intensity productive spaces, literally mushrooming worldwide, manufacturing buildings establish spatial relations with a whole series of facilities that are fundamental to support not only modern production but also contemporary urban life. Starting from a database of selected case studies we retrieve geospatial coordinates and generate analysis of these places based on the collection and elaboration of Location-Based Social Network (LBSN) data and generate visual maps at high spatial resolution through information technologies. These models allow us to assess different case studies retrieving their common and specific urban features in terms of accessibility, circulation, programming, crowding, density and socio-economic differ-entiation. Our methodology aims thus to reveal meaningful differences and commonalities both in terms of global spatial design characteristics and their local adaptations. Finally, our research focuses on interpreting, integrating and visualizing these models through AI based systems, particularly Natural Language Processing. Recent studies in urban research have proved NLP systems to be proficient at matching and correlating heterogeneous data, despite differences in format, scale, or terminology, thus providing a cohesive and dynamic understanding of cities that can support the development of new frameworks for urban analysis and design transformations. AI based techniques, in conjunction with urban big data analytics, provide investigations that were hitherto restricted by data limits. This capacity is especially vital in urban environments, where the intricacy and fluidity of urban systems require advanced analytical instruments.