Artificial Intelligence for territorial interpretation: from image clustering to perceptual mapping

The research investigates artificial intelligence as a device for the automatic interpretation of landscape, reframing representation not as a neutral reproduction but as a cognitive operation in which perception, description, and evaluation converge. Moving from the assumption that landscape is not an objective given but a culturally and perceptually constructed form, the study proposes a fully data-driven methodology based on geolocated images. Through a systematic grid sampling, street-level panoramic views are collected and processed within a multimodal pipeline integrating visual analysis, language models, and multi-agent evaluation. Images are first translated into textual descriptions and semantically clustered, allowing territorial classes to emerge from the data rather than from predefined taxonomies. In parallel, a simulated cognitive framework, structured through four agent profiles, produces evaluative scores and textual judgments, later analysed through sentiment detection. The integration of these layers generates a georeferenced dataset from which a perceptual cartography of the territory is constructed. Applied to the urban context of San Giustino (Italy), the method reveals a continuous gradient from dense urban cores to rural landscapes, while exposing differentiated perceptual readings across observer profiles. Within this framework, artificial intelligence does not replace human interpretation; it operates as an epistemic extension, transforming the landscape into a distributed field of comparable perceptions, where representation becomes a computable form of shared knowledge.