Beyond Automatism: Drawing as a Construction of Meaning in the Representation of Complex Heritage

Abstract

The rapid transformation of image acquisition and processing has weakened the traditional link between visual representation and the observed phenomenon, requiring a reconsideration of Drawing within contemporary heritage research. Images now emerge from multisensor systems that generate complex and multilayered visual forms. Understanding them means understanding the conditions and operations that structure them, distinguishing what derives from the phenomenon from what results from sensors, software or modelling procedures. Vision becomes a mediated process whose internal plurality must be made visible to allow critical interpretation.
In this framework, Drawing acquires a renewed centrality. No longer a mere figurative translation, it operates as a method of organising visual knowledge, selecting and correlating heterogeneous information into legible configurations. It constructs relations, guides interpretation and defines scales and frames that data alone cannot decide. From this perspective, the layered image is introduced as a visual form that integrates measurement, geometry, semantics, verification and contextual information, making the genealogy of representation explicit.
Complex architecture reveals the limits of automation: irregularities and stratifications show that computation can align but not interpret. Generative models further expose this ambiguity, producing plausible yet unfounded images. Drawing, by contrast, transforms data into knowledge and orients automatisms rather than merely accepting them. Data-driven imagery therefore reinforces the need for a graphic science capable of guiding and interpreting an increasingly information-rich reality.

DOI: https://doi.org/10.20365/disegnarecon.36.2026.13