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Author: Valerio Palma, PhD candidate in Architectural, History and Design, Politecnico di Torino.

Project: ARETHA
Research Team: Matteo Robiglio, Claudio Casetti, Francesca Frassoldati, Valerio Palma


In recent years, the diffusion of large image datasets and an unprecedented computational power have boosted the development of a class of artificial intelligence algorithms referred to as deep learning (DL). Among DL methods, convolutional neural networks (CNNs) have proven particularly effective in computer vision, finding applications in many disciplines.

While AI is just beginning to interact with the built environment through mobile devices, heritage technologies have long been producing and exploring digital models and spatial archives. The interaction between DL algorithms and state-of-the-art information modeling is an opportunity to both exploit spatial data collections and optimize new object recognition techniques.

We introduce a project aimed at studying CNNs applications in the field of architectural heritage, a still to be developed research stream. The first steps and results in the development of a mobile app to recognize monuments are discussed in the recent paper Towards deep learning for architecture: a monument recognition mobile app. The paper was presented at the 8th International Workshop 3D-ARCH in Bergamo (Italy) and it is published in the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

The full paper is available at

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Future Urban Legacy Lab