Volume XLII-2/W15
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W15, 77–84, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W15-77-2019
© 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/W15, 77–84, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W15-77-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Aug 2019

19 Aug 2019

ARCHITECTURE RECOGNITION BY MEANS OF CONVOLUTIONAL NEURAL NETWORKS

L. N. Andrianaivo1,2, R. D'Autilia2, and V. Palma1 L. N. Andrianaivo et al.
  • 1FULL, the Future Urban Legacy Lab, Politecnico di Torino, Via Agostino da Montefeltro 2, 10134 Torino, Italy
  • 2Dipartimento di Matematica e Fisica, Università degli Studi Roma Tre, Largo San Leonardo Murialdo 1, 00146 Roma, Italy

Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Convolutional Neural Networks, Image Classification, Architectural Heritage, Mobile Computing

Abstract. The use of mobile computing technologies can change the experience of visiting cultural sites by making vast digital heritage collections accessible on site. The spread of machine learning technologies on mobile devices is encouraging the interaction of artificial intelligence with the shape of the built environment. However, while some research already applies deep learning image recognition in an urban context, the literature on how to develop effective neural networks to detect architectural features is still limited, as well as the availability of architecture-related datasets. This work presents the steps and results of the prototype development of a mobile app to perform monument recognition using convolutional neural networks. The tool allows users to interact with the physical space and access a digital archive of texts, models, images and other data.