MonuMAI Citizen Science Project Dataset

The latest Deep Learning (DL) models for detection and classification have achieved an unprecedented performance over classical machine learning algorithms. However, DL models are black-box methods hard to debug, interpret, and certify. DL alone cannot provide explanations that can be validated by a non technical audience. In contrast, symbolic AI systems that convert concepts into rules or symbols -- such as knowledge graphs -- are easier to explain. However, they present lower generalisation and scaling capabilities. A very important challenge is to fuse DL representations with expert knowledge. One way to address this challenge, as well as the performance-explainability trade-off is by leveraging the best of both streams without obviating domain expert knowledge. We tackle such problem by considering the symbolic knowledge is expressed in form of a domain expert knowledge graph. We present the eXplainable Neural-symbolic learning (X-NeSyL) methodology, designed to learn both symbolic and deep representations, together with an explainability metric to assess the level of alignment of machine and human expert explanations. 

MonuMAI database contains 1.514 RGB images of monument facades of four architectural styles Hispanic-Muslim, Gothic, Renaissance, and Baroque. Where the used images for building Monumai were selected so that the monument is centered, fill most of the image, and have high quality so the monument features can be observable.

MonuMAI dataset includes 6650 annotated key elements distributed into the fifteen architectural element classes. A statistic analysis of these elements is shown in the Table.Detection annotation is provided in Pascal VOC format XML.

MonuMAI app

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Terms of use

The annotation in this dataset along with the images are licensed under a Creative Commons Attribution 4.0 License.