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Publications CHNT 24, 2019

For papers: long paper (4-10 pages, up to 10 illustrations, deadline for submission: January 31, 2020) – peer reviewed short paper (=long abstract) – not peer reviewed no publication For round table contributions: short paper (=long abstract) – not peer reviewed no publication For posters: short paper (=long abstract) – not peer reviewed no...

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Teaching Digital Methods

Call for Short Papers Nadine Alpino1 │ Stephen Stead2(1DOKU PLUS S.à r.l., Luxembourg | 2Paveprime Ltd., UK) Keywords: teaching, digital methods, exchange Every year we see presentations of fantastic results at CHNT which are, besides being traditional research, products of a range of digital techniques. We also see such digital methods becoming popular in spheres like of art and museums. It almost seems like everybody understands how these approaches work: but is that really true?  Have you ever tried to get a definition of a “point cloud” from a cultural heritage professional?Digital methods are still relatively new in the study of art history and some sub-disciplines of archaeology. So, how do we establish, teach and familiarise people who are as yet far from digital methods with their potential? Where are the sources of introductory material for the uninitiated on the fields of photo- and laser scanning, GIS, 3D-reconstruction and the myriad of other new techniques? How can we deliver an idea of the possibilities or create hand-on workshops that will inspire students and professionals alike?These and similar questions are the topics for our round table. We hope to discuss, exchange experiences and get an idea of the needs of the students in these disciplines. Most importantly we want to consider how to take this forward, perhaps by starting or joining an existing exchange platform, co-working with special interest groups or building a network of likeminded professionals. Time extent180 Minutes Duration of presentationsMaximum of 10 Minutes Target group(future) teachers, students, archaeologists, art historians, historians, members of special interest groups SubmissionMind the new...

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Visitor-Centered Intelligence for Cultural Heritage Sites

Call for Papers Andi SMART | Pikakshi MANCHANDA | David ROSS | Cristina MOSCONI(University of Exeter, United Kingdom) Keywords: Visitor Intelligence, Audience Research, Visitor Experience, Artificial Intelligence, Data Analytics Obtaining intelligence on visitor motivations and behaviours is an essential activity in the creation and improvement of visitor experiences. Innovative digital technologies provide affordances for the collection and analysis of visitor data that have previously not been possible. New approaches, capitalising on digital technologies, provide rich insights into visitor profiles, behaviour and experience, and help identify target audiences and inform the design of new interpretative experiences. These insights provide opportunities to compliment, and extend established methods for Audience Research. Key themes may include: Geospatial analysis – data collected using tracking devices, which offer insights into users’ dwell times at areas of interest, and the visitor journey;On/off site visitor surveys can provide psychographic, socio-demographic and experience assessment of visitor segments;Natural Language Analysis – a growing body of data from social media platforms can be analysed using Natural Language Processing (NLP) tools in order to understand visitor sentiment;Speech Recognition/Analysis – speech recognition tools support the automatic transcription of oral feedback recorded at the visitor’s convenience, allowing the analysis of comments;Visual Attention and Fixation – visitor gaze time data collected using eye tracking devices (Pupil Centred Corneal Reflection), which can provide evidence into popular site artefacts;Experiential Analysis – new ways of articulating the visitor experience. This may include experiential interactions, experiential outcomes and emotions. The aforementioned sources and methods provide an accessible way for Cultural Heritage Site managers to gain a comprehensive picture of their visitor profiles and experience, and assess which aspects require more attention. This session is open to papers (comparative or case-based studies) focusing on the challenges encountered in analysing visitor data in terms of lack of resources, datasets or digital privacy and data protection concerns, as well as computational approaches for capturing and analysing such data to obtain a better understanding of visitor motivations, interactions and experiences. SubmissionMind the new...

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Archaeological Prospection by Lidar Beyond Simple Hillshading

Call for Papers Irmela HERZOG1 | Michael DONEUS2(1LVR-Amt für Bodendenkmalpflege im Rheinland, Germany | 2University of Vienna, Austria) Keywords: Archaeological Prospection; Lidar; Digital Elevation Model For more than a decade, Lidar data has been used to detect and delimit archaeological sites by highlighting subtle altitude differences generated by the remains of these sites. In several European countries ordnance survey institutions nowadays provide Lidar data for archaeological purposes free of charge, and sometimes web map services are available that show hillshading views of this elevation data. Some researchers have pointed out the drawbacks of the ordnance survey Lidar data in their study area, favouring Lidar data acquisition commissioned by archaeologists. The latter procurement approach is the only option eligible in countries where official Lidar data is not accessible by archaeologists. In densely vegetated regions, filtering of the Lidar data is an issue. Additional issues include the accuracy of the measurements, irregular point density after filtering as well as combining data acquired in different campaigns or Lidar data with results of other prospection methods. Besides simple hillshading, several visualisation methods have been proposed that enhance detectability of specific archaeological features. Recently, pattern recognition and machine learning approaches have been used for the (semi-)automatic detection of sites in Lidar data, allowing to scan large regions with the aim of identifying sites of a predefined site type. The aim of this session is to show the potential of Lidar data beyond simple hillshading by papers focusing on: Best practice of Lidar data acquisition for archaeological purposesData filtering in densely vegetated regionsComparison of Lidar with SfM approaches in areas with hardly any vegetationPotential and limits of different visualisation approachesMonitoring sites by comparing Lidar data acquired in different yearsCombining Lidar data with data derived from other prospection methods(Semi-)automatic detection of sites in Lidar data for instance by machine learning approaches. SubmissionMind the new...

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