Volume XLII-2/W9
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W9, 551-556, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W9-551-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W9, 551-556, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W9-551-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  31 Jan 2019

31 Jan 2019

TOWARDS DEEP LEARNING FOR ARCHITECTURE: A MONUMENT RECOGNITION MOBILE APP

V. Palma V. Palma
  • FULL_Future Urban Legacy Lab, Politecnico di Torino, Torino, Italy

Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Convolutional Neural Networks, Architectural Heritage, Mobile Apps, Information Modeling

Abstract. In recent years, the diffusion of large image datasets and an unprecedented computational power have boosted the development of a class of artificial intelligence (AI) 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. This paper introduces a project aimed at studying CNN techniques 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. 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 addressed, as an opportunity to both exploit heritage collections and optimize new object recognition techniques.