Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuable insights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable for research.

Digitality is a cause and a consequence of different data cultures. It applies to the 10 research projects that are included in this volume. They are rooted in various humanities disciplines such as art history, philosophy, musicology, religious studies, architectural history, media studies, and literature studies. As diverse as the disciplines are the objects and their formats, which are the subject of this book. The cultural data of the projects include recordings of music and spoken word, photographs and other types of images, handwriting, typoscripts and maps. The oldest material dates back to 500 BCE, followed by medieval times, the 18th and 19th centuries, early 20th century and the present. All projects share that they study their material with digital methods, although digitality comes into play at different moments and layers in each of the projects. Hardly readable manuscripts from the 18th century have to be treated with specialized OCR-methods while Plato’s texts are already available in digital form, and therefore open up other affordances for analysis. Special analysis possibilities had to be developed for certain image sources. For all projects, however, it is equally true that only the digitization of the objects makes them accessible to the methods that are the subject of this book.

Throughout history, the access to large bodies of water facilitated the foundation, rise and prosperity of human settlements. Besides the vital quality of fresh water, the connecting potential of water was the decisive factor that prompted these developments. Since centuries, oceans, seas, rivers and lakes allowed for transportation, exchange and trading. Thereby, they connected places around the world and enabled a direct connection between different cities, states and cultures on a global scale. Over time, these distant places faced similar challenges and took part in related developments. People around the world developed ports, warehouses, and other facilities to accommodate rapidly changing types of ships and containers, from papyrus boats to sail ships and super tankers; and neighboring cities had to contemporaneously adapt to new technologies, flows of goods, people and ideas.

Historical photos of towns and villages contain a great deal of information about the built environment of the past. However, it is difficult to evaluate the information of images that are not labeled or incorrectly labeled or not organized in repositories or collections. In order to make the sheer volume of images that are not tagged with metadata found on the Internet or in institutional archives accessible for research, an automated recognition of the image content, in this case of buildings, is necessary. Computer vision can help to address this problem and enable the identification of historical image content. This article describes how artificial intelligence and crowdsourcing are used to identify buildings in nearly half a million historical images of the city of Amsterdam. It explains how computer science and humanities disciplines are linked together to accomplish this task.

Water has served and sustained societies throughout the history of humankind. People have actively shaped its course, form, and function for human settlement and the development of civilizations. Around water, they have created socioeconomic structures, policies, and cultures; a rich world of narratives, laws, and practices; and an extensive tangible network of infrastructure, buildings, and urban form. Today, the complex and diverse systems of the past are necessarily the framework for preservation and reuse as well as for new systems. Through twenty-one chapters in five thematic sections, this book links the practices of the past to a present in which heritage and water are largely two separate disciplinary and professional fields. It describes an alternative emerging present in which policymaking and design work together to recognize and build on traditional knowledge and skills while imagining how such efforts will help us develop sustainable futures for cities, landscapes, and bodies of water.

Digital technologies provide novel ways of visualizing cities and buildings. They also facilitate new methods of analyzing the built environment, ranging from artificial intelligence (AI) to crowdsourced citizen participation. Digital representations of cities have become so refined that they challenge our perception of the real. However, computers have not yet become able to detect and analyze the visible features of built structures depicted in photographs or other media. Recent scientific advances mean that it is possible for this new field of computer vision to serve as a critical aid to research. Neural networks now meet the challenge of identifying and analyzing building elements, buildings and urban landscapes. The development and refinement of these technologies requires more attention, simultaneously, investigation is needed in regard to the use and meaning of these methods for historical research. For example, the use of AI raises questions about the ways in which computer-based image recognition reproduces biases of contemporary practice. It also invites reflection on how mixed methods, integrating quantitative and qualitative approaches, can be established and used in research in the humanities. Finally, it opens new perspectives on the role of crowdsourcing in both knowledge dissemination and shared research. Attempts to analyze historical big data with the latest methods of deep learning, to involve many people—laymen and experts—in research via crowdsourcing and to deal with partly unknown visual material have provided a better understanding of what is possible. The article presents findings from the ongoing research project ArchiMediaL, which is at the forefront of the analysis of historical mediatizations of the built environment. It demonstrates how the combination of crowdsourcing, historical big data and deep learning simultaneously raises questions and provides solutions in the field of architectural and urban planning history.

It was the present moment. No one need wonder that Orlando started, pressed her hand to her heart, and turned pale. For what more terrifying revelation can there be than that it is the present moment? That we survive the shock at all is only possible because the past shelters us on one side and the future on another. But we have no time now for reflections.(Virginia Woolf, Orlando)How long does the present moment last? Where and when does the past begin and how does the present end? In physics-or more precisely in the special theory of relativity-the present can be defined as the coordinate origin in a spacetime diagram- A n unextended point that separates an observer's past and future light cones. From that point of view, the present has no duration at all; the past instantly assimilates the future without any hesitation in between. However, time perception tells us that we actually experience a 'here and now'. Psychologists believe that the time range we perceive as the present, the socalled specious present, lasts about three seconds-the interval duration after which the brain may be said to reset its attention. This is already infinitely more than no duration at all but this recognition is still not enough to explain concepts like the present time or 'today' as an indicator of the contemporary. In the domain of history, the present seems to be a much more complex construction. When we speak of phenomena as contemporary, we place them in an extended present. We concede that the present encompasses the recent past and the near future- A temporal range that provides a stage for the actions and reactions that shape our world.

The ArchiMediaL project aims to bridge between data science and researches on contemporary and historical built environments by developing state of the art AI algorithms for the automatic linking of available meta-data and image repositories. As a case-study we use the 360,000+ historical images from the Amsterdam Beeldbank database.

Creative practices are at the heart of human history. Humans have responded creatively to a broad range of challenges—environmental, political, economic, social, religious, cultural—by shaping our tangible physical environment and our intangible cultural practices. Our present buildings, cities, and landscapes as well as our local cultures, socio-cultural behaviours, and lifestyles are the outcome of creative practices of many centuries. The structures of the past, their practices, and representations are part of our heritage. They condition how we live today and how we plan for tomorrow. To move forward, we must understand the how and why, the who and where, of past creative practices that have created our contemporary environment.

Built form dominates the urban space where most people live and work and provides a visual reflection of the local, regional and global esthetical, social, cultural, technological and economic factors and values. Street-view images and historical photo archives are therefore an invaluable source for sociological or historical study; however, they often lack metadata to start any comparative analysis. Date and location are two basic annotations often missing from historical images. Depending on the research question other annotations might be useful, that either could be visually derived (e.g. the number or age of cars, the fashion people wear, the amount of street decay) or extracted from other data sources (e.g. crime statistics for the neighborhood where the picture was taken). Recent advances in automatic visual analysis and the increasing amount of linked open data triggered the research described in this paper. We provide an overview of the current status of automated image analysis and linked data technology and present a case study and methodology to automatically enrich a large database of historical images of buildings in the city of Amsterdam.

The 2019 International Conference "Water as Heritage" took place 27-31 May 2019 in Chiayi, Taiwan. Organized by the Taiwan International Institute for Water Education, the International Council on Monuments and Sites (ICOMOS) Netherlands and the Leiden-Delft-Erasmus Centre for Global Heritage and Development, it brought together key water-focused organizations and heritage groups with a concern for water-related heritage in order to develop networks and build working relationships across the diversity of sectors and disciplinary fields. With participants from 25 countries and six continents, the conference explored the mutual benefits arising from such collaborative efforts. A key issue of the conference was the consideration of water-related heritage as an essential element in addressing current and future challenges of water management.

The article reflects the state of mathematics between the natural sciences and the humanities. By arguing that mathematics is a humanities subject, it suggests a close connection between mathematics and urban morphology studies. This also applies to the discrepancy between quantitative and qualitative methodological approaches. New types of research based on quantitative methods reveal previously unknown aspects of urban phenomena. They will play an increasingly important role in future research, and it is a challenge for the humanities to effectively integrate mathematical perspectives on the human habitat.

We address the interpretability of convolutional neural networks (CNNs) for predicting a geo-location from an image. In a pilot experiment we classify images of Pittsburgh vs Tokyo and visualize the learned CNN filters. We found that varying the CNN architecture leads to variating in the visualized filters. This calls for further investigation of the effective parameters on the interpretability of CNNs.