Classification of Architectural Heritage Images Using Deep Learning Techniques

Abstract

Abstract:The classification of the images taken during the measurement of an architectural asset is an essential task within the digital documentation of cultural heritage. A large number of images are usually handled, so their classification is a tedious task (and therefore prone to errors) and habitually consumes a lot of time. The availability of automatic techniques to facilitate these sorting tasks would improve an important part of the digital documentation process. In addition, a correct classification of the available images allows better management and more efficient searches through specific terms, thus helping in the tasks of studying and interpreting the heritage asset in question. The main objective of this article is the application of techniques based on deep learning for the classification of images of architectural heritage, specifically through the use of convolutional neural networks. For this, the utility of training these networks from scratch or only fine tuning pre-trained networks is evaluated. All this has been applied to classifying elements of interest in images of buildings with architectural heritage value. As no datasets of this type, suitable for network training, have been located, a new dataset has been created and made available to the public. Promising results have been obtained in terms of accuracy and it is considered that the application of these techniques can contribute significantly to the digital documentation of architectural heritage.

Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage

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Deep Learning Architect: Classification for Architectural Design Through the Eye of Artificial Intelligence

“…In addition, space is experienced not only through our perceptions but also through our other senses. In terms of techniques, most previous literature employs clustering and learning of local features (Shalunts et al, 2011), but not deep learning (Llamas et al, 2017). This paper attempts to classify designs of modern and contemporary architecture using a deep convolutional neural network.…”

CNN-Based Watershed Marker Extraction for Brick Segmentation in Masonry Walls

“…in a wide range of fields from medical images [14,15], scene understandings [13] or autonomous driving [1], we only find a few references yet for application of DL methods in architecture or cultural heritage documentation. In addition, existing methods [3,6] use deep learning rather for classification of the available images of the architectural heritage, instead of segmentation and feature extraction for detailed analysis.…”