Japanese early-modern woodblock prints depicting pastoral views of the countryside, so-called meisho-e (images of famous places), are often defined today as landscapes (fūkei). However, the notion of fūkei is a modern cultural translation, which obscures specificities of Japanese visual culture, and intricacies of early modern spatiality or a socially produced space. To uncover these characteristics and provide a more nuanced understanding of meisho-e prints, we have engaged in a macroanalytical study of relationships between places depicted in prints and actual topography, aided by computational technologies rooted in Natural Language Processing (NLP). In our prior work, we experimented with automated harvesting of geospatial data from image-content-related inscriptions on two hundred prints. In this follow-up work, we undertake a large-scale automated mapping of meisho and we study the geographical distribution of sites featured in these prints. We explore two different computational paths, one using deep learning and one based on digital gazetteers, and reflect on the challenges and benefits of the applied computational approaches. We improve the former, which was the state-of-the-art, using pre-training, and we show that the latter is beneficial in terms of mapping. Finally, by using automatically extracted place-name entities, we undertake an analysis of prints over space and time. We release our code and the dataset for public use: https://github.com/Connalia/ai-jan-art.

As new to the Swedish research system, one is faced with a series of questions, about what applies to qualifications, what the networks look like, but also practical issues. To make things easier, YAS has developed a guide for international researchers, to help navigate Swedish academia and remove time-consuming obstacles.

This paper contributes to studying relationships between Japanese topography and places featured in early modern landscape prints, so-called ukiyo-e or ‘pictures of the floating world’. The printed inscriptions on these images feature diverse place-names, both man-made and natural formations. However, due to the corpus’s richness and diversity, the precise nature of artistic mediation of the depicted places remains little understood. In this paper, we explored a new analytical approach based on the macroanalysis of images facilitated by Natural Language Processing technologies. This paper presents a small dataset with inscriptions on prints that have been annotated by an art historian for included place-name entities. Our dataset is released for public use. By fine-tuning and applying a Japanese BERT-based Name Entity Recogniser, we provide a use-case of a macroanalysis of a visual dataset that is hosted by the digital database of the Art Research Center at the Ritsumeikan University, Kyoto. Our work studies the relationship between topography and its visual renderings in early modern Japanese ukiyo-e landscape prints, demonstrating how an art historian’s work can be improved with Natural Language Processing toward distant viewing of visual datasets. 

This paper investigates the application of Natural Language Processing as a means to study the relationship between topography and its visual renderings in early modern Japanese ukiyo-e landscape prints. We introduce a new dataset with titles of landscape prints that have been annotated by an art historian for any included place-names. The prints are hosted by the digital database of the Art Research Center at the Ritsumeikan University, Kyoto, one of the hubs of Digital Humanities in Japan. By applying, calibrating and assessing a Named Entity Recognition (NER) tool, we argue that ‘distant viewing’ or macroanalysis of visual datasets can be facilitated, which is needed to assist art historical studies of this rich, complex and diverse research material. Experimental results indicated that the performance of NER can be improved by 30% and reach 50% precision, by using part of the introduced dataset.

This paper creates a conceptual frame and explanatory point of reference for the collection of papers presented at the exploratory workshop “Data Science for Digital Art History: Tackling Big Data Challenges, Algorithms, and Systems” organized at the KDD 2018 Conference in Data Mining and Knowledge Discovery held in London in August 2018. The goal of the workshop was to probe the field and to build a constructive interdisciplinary dialogue between two research areas: Data Science and Art History. The workshop’s chairs and the authors of this paper share the conviction that Data Science can enrich art studies while analysis of visual data can have a positive impact on Data Science. Thus, the research initiative tried to critically reflect on the interdisciplinary collaboration between diverse research communities and its epistemological and ontological effects.

Wetlands are often vital physical and social components of a country's natural capital, as well as providers of ecosystem services to local and national communities. We performed a network analysis to prioritize Sustainable Development Goal (SDG) targets for sustainable development in iconic wetlands and wetlandscapes around the world. The analysis was based on the information and perceptions on 45 wetlandscapes worldwide by 49 wetland researchers of the Global Wetland Ecohydrological Network (GWEN). We identified three 2030 Agenda targets of high priority across the wetlandscapes needed to achieve sustainable development: Target 6.3-Improve water quality; 2.4-Sustainable food production; and 12.2-Sustainable management of resources. Moreover, we found specific feedback mechanisms and synergies between SDG targets in the context of wetlands. The most consistent reinforcing interactions were the influence of Target 12.2 on 8.4-Efficient resource consumption; and that of Target 6.3 on 12.2. The wetlandscapes could be differentiated in four bundles of distinctive priority SDG-targets: Basic human needs, Sustainable tourism, Environmental impact in urban wetlands, and Improving and conserving environment. In general, we find that the SDG groups, targets, and interactions stress that maintaining good water quality and a wise use of wetlandscapes are vital to attaining sustainable development within these sensitive ecosystems.