Analysis of the new architectural dataset NeoFaçade and its potential in machine learning

Abstract

The presence of artificial intelligence (AI) in architecture has been growing rapidly in recent years. The collaboration between architects and AI developers has led to significant improvements in various design applications. Further development of machine learning techniques is highly dependent on the availability of large, structured datasets. The aim of the article is to demonstrate the potential of a novel dataset, NeoFaçade, which contains annotated pictures of historical tenements. A comparison of the dataset with existing benchmark datasets, the CMP Façade and the Paris Art-Deco datasets, highlights its exceptional features. Its applications in three machine learning tasks are also presented: semantic segmentation, image translation and image generation. In all three tasks, the models trained with NeoFaçade provide satisfactory results and indicate the great potential of this collection. The planned further development of the dataset will allow the training of more precise models that will be able to distinguish more elements and features of the façades and assist architects in designing tenements.