# Classification of Architectural Heritage Images Using Deep Learning Techniques
## 1 Introduction
### Image Classification  
In this article, we apply deep learning techniques to the classification of images for the documentation of the architectural cultural heritage. We specifically study the use of the most representative convolutional neural networks to extract useful information from images. The main objective is to verify the real usefulness of some of these networks that set the current state of the art for their application in the classification of images of heritage buildings.  
There is much literature of different applications of deep learning in the classification of images, both generic [12; 13; 14; 15; 16; 17; 18], and specific, such as aerial images [19; 20], medical images [21], license plate and vehicle recognition [22], gait recognition [23], classification of microorganisms [24], recognition of the urban environment [25], fruit recognition [26] and many more. There is also literature concerning the classification of images of architectural heritage, but using other techniques such as pattern detection [27], instance retrieval [28], Gabor filters and support vector machine [29], computer vision algorithms [11], clustering and learning of local features [30], hierarchical sparse coding of blocklets [31], or Multinomial Latent Logistic Regression [32]; but no references concerning the classification of images of architectural heritage are known using deep learning, except a prior publication of the authors [33]. Another of the topics studied is the evaluation of whether, for these tasks, it is better to train a network from scratch (full training) or to use fine tuning of a pre-trained network. There is a reference that analyzes this specific aspect in medical images [21] and something similar in images of food [34; 35], but no bibliography in the field of architectural heritage is known.  
There are few datasets of images in the realm of historical heritage (to date only one of architectural styles has been found [32]), which has motivated the creation of one of our own focused on architectural elements. This dataset aims to be the origin of a system which could be useful for the training of neural convolutional networks or other techniques of classification in this type of task. The absence of datasets prevents the comparison between different techniques and works, which is why, by way of comparison, these techniques have also been evaluated using the indicated dataset of architectural styles in order to verify that the results obtained are superior to other classical classification methodologies.  
In the labeling of images considered in this article, the goal is to automatically deduce the main element to be reflected in each image. In this way, it is possible to organize the available images in different categories and facilitate the work of the specialists or enthusiasts who search through the collection by consulting very precise keywords. The applications of these techniques in documentation tasks are many: web and mobile applications to access and consult details of a heritage building, study of the different elements of a building in different geographical areas and different times, study of their evolution, development of systems that could be trained to detect historical periods or architectural styles, reveal pathologies, find alterations or previous interventions, search for similar images in other sites, etc.