Volume XLII-2/W15
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W15, 343–350, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W15-343-2019
© 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, 343–350, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W15-343-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  21 Aug 2019

21 Aug 2019

ARCHITECTURAL HERITAGE RECOGNITION IN HISTORICAL FILM FOOTAGE USING NEURAL NETWORKS

F. Condorelli1, F. Rinaudo1, F. Salvadore2, and S. Tagliaventi2 F. Condorelli et al.
  • 1DAD, Department of Architecture and Design, Politecnico di Torino, Lab G4CH – Laboratory of Geomatics for Cultural Heritage, Italy
  • 2CINECA – HPC Department, Italy

Keywords: Deep Learning, Neural Networks, TensorFlow, Photogrammetric Workflow, Cultural Heritage, Historical Video Classification

Abstract. Researching historical archives for material suitable for photogrammetry is essential for the documentation and 3D reconstruction of Cultural Heritage, especially when this heritage has been lost or transformed over time. This research presents an innovative workflow which combines the photogrammetric procedure with Machine Learning for the processing of historical film footage. A Neural Network is trained to automatically detect frames in which architectural heritage appears. These frames are subsequently processed using photogrammetry and finally the resulting model is assessed for metric quality. This paper proposes best practises in training and validation on a Cultural Heritage asset. The algorithm was tested through a case study of the Tour Saint Jacques in Paris for which an entirely new dataset was created. The findings are encouraging both in terms of saving human effort and of improvement of the photogrammetric survey pipeline. This new tool can help researchers to better manage and organize historical information.