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 Visit https://monumai.ugr.es Terms of use The annotation in this dataset along with the images are licensed under a Creative Commons Attribution 4.0 License.最新的深度学习（Deep Learning, DL）模型在检测与分类任务上已取得远超经典机器学习算法的空前性能。然而，深度学习模型属于黑箱方法，难以调试、解释与验证，仅依靠深度学习无法提供可被非技术受众验证的解释。与之相对，将概念转化为规则或符号的符号人工智能系统——例如知识图谱——更易于解释，但它们的泛化与扩展能力相对有限。将深度学习表征与领域专家知识相融合是一项极具挑战性的课题，而兼顾两者优势且不摒弃领域专家知识，正是解决该挑战以及平衡性能与可解释性矛盾的可行途径之一。本研究将符号知识以领域专家知识图谱的形式进行建模，以此处理上述问题，并提出了可解释神经符号学习（eXplainable Neural-symbolic learning, X-NeSyL）方法论，该方法旨在同时学习符号表征与深度学习表征，并配套可解释性指标以评估机器解释与人类专家解释的对齐程度。 MonuMAI数据库包含1514张建筑立面的RGB图像，涵盖西班牙-伊斯兰式、哥特式、文艺复兴式以及巴洛克式四种建筑风格。构建该数据集时所选用的图像均经过筛选，要求建筑主体居中、占据图像大部分区域且画质清晰，以确保建筑特征可被清晰观测。 MonuMAI数据集共包含6650个标注关键元素，分为15个建筑元素类别，相关元素的统计分析详见附表。检测标注采用Pascal VOC格式的XML文件。 MonuMAI应用 访问网址：https://monumai.ugr.es 使用条款 本数据集的标注内容与图像均采用知识共享署名4.0许可协议（Creative Commons Attribution 4.0 License）进行授权。