A Neural Networks Approach to Detecting Lost Heritage in Historical Video
Francesca Condorelli|Fulvio Rinaudo|Francesco Salvadore|Stefano Tagliaventi
Documenting Cultural Heritage through the extraction of 3D measures with photogrammetry is fundamental for conservation memory past. However, when heritage has been lost only way to recover this information use historical images from archives. The aim study experiment new ways search architectural in video material and save effort operator archive terms efficiency time. A workflow proposed automatically detect film footage using Deep Learning find suitable process its virtual reconstruction. performance network was tested on two case studies considering different scenarios, Tour Saint Jacques which still exists tuning networks, Les Halles test algorithms a real an architecture destroyed. Despite poor quantity low quality available training network, it demonstrated that, few frames, possible reach same results trained large dataset. Moreover, introduction metrics based time intervals measure saving human achieved. These findings represent important innovation documentation destroyed monuments open about
ISPRS international journal of geo-information