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Table of contents (5 chapters)
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Front Matter
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Back Matter
Keywords
About this book
The case studies showcase the strengths and weaknesses of semi-automatic and automatic approaches by testing them in a symbol recognition task on the same scanned map. Chapter 3 presents the technical challenges and trends in building a map processing, modeling, linking, and publishing framework. The framework will enable querying historical map collections as a unified and structured spatiotemporal source in which individual geographic phenomena (extracted from maps) are modeled (described) with semantic descriptions and linked to other data sources (e.g., DBpedia, a structured version of Wikipedia). Chapter 4 dives into the recent advancement in deep learning technologies and their applications on digital map processing. The chapter reviews existing deep learning models for their capabilities on geographic feature extraction from historical maps and compares different types of training strategies. A comprehensive experiment is described to compare different models and their performance.
Historical maps are fascinating to look at and contain valuable retrospective place information difficult to find elsewhere. However, the full potential of historical maps has not been realized because the users of scanned historical maps and the developers of digital map processing technologies are from a wide range of disciplines and often work in silos. Each chapter in this book can be read individually, but the order of chapters in this book helps the reader to first understand the “product requirements” of a successful digital map processing system, then review the existing challenges and technologies, and finally follow the more recent trend of deep learning applications for processing historical maps.
The primary audience for this book includes scientists and researchers whose work requires long-term historical geographic data as well as librarians. The secondary audience includes anyone who loves maps!
Reviews
“This book may serve as a useful text for a variety of academic settings, including in advanced GIS seminars where students are expected to have a basic understanding of geospatial technologies and apply that knowledge towards more advanced projects. As the book endeavours to help readers understand how to bridge connections between users and producers of digital map technologies, it could also be well-suited for a user experience/UX course.” (Hannah Gunderman, The Globe, Vol. 87 (1), 2020)
Authors and Affiliations
About the authors
Weiwei Duan is a Ph.D. student major in Computer Science at the University of Southern California (USC). She is working on building a computer-vision-based system for extracting information on georeferenced images and storing them in a structured format for analysis. The system localizes geographic objects on images by integrating geospatial information and using limited noisy labeling data. Her research interests are computer vision, knowledge graphs, and machine learning.
Stefan Leyk is an Associate Professor at the Department of Geography, University of Colorado Boulder and a Research Fellow at the Institute of Behavioral Science. He is a Geographical Information Scientist with research interests in information extraction, spatio-temporal modeling and socio-environmental systems. In his work he uses various sources of historical spatial data to better understand the evolution of human systems and how the built environment interacts with environmental processes in the context of land use and natural hazards.
Johannes H. Uhl is a PhD candidate at the Department of Geography, University of Colorado, Boulder, USA. He received his MS degree in Geomatics, Geodesy, and Cartography from Karlsruhe University of Applied Sciences, Germany, and from Polytechnic University of Valencia, Spain, in 2011. His current research interests include spatiotemporal information extraction and data modeling, uncertainty analysis, geospatial data integration, and machine learning. He uses a variety of spatial-temporal datasets, such as remote sensing data and derived data products, historical topographic maps, or large real-estate related databases.
Craig A. Knoblock is Executive Director of the Information Sciences Institute of the University of Southern California (USC), Research Professor of both Computer Science and Spatial Sciences at USC, Research Director of the Center on Knowledge Graphs, and Associate Director of the Informatics Program at USC. He received his Bachelor of Science degree from Syracuse University and his Master’s and Ph.D. from Carnegie Mellon University in computer science. His research focuses on techniques for describing, acquiring, and exploiting the semantics of data. He has worked extensively on source modeling, schema and ontology alignment, entity and record linkage, data cleaning and normalization, extracting data from the Web, and combining all of these techniques to build knowledge graphs. He has published more than 300 journal articles, book chapters, and conference papers on these topics and has received 7 best paper awards on this work. Dr. Knoblock is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Fellow of the Association of Computing Machinery (ACM), past President and Trustee of the International Joint Conference on Artificial Intelligence (IJCAI), and winner of the 2014 Robert S. Engelmore Award.
Bibliographic Information
Book Title: Using Historical Maps in Scientific Studies
Book Subtitle: Applications, Challenges, and Best Practices
Authors: Yao-Yi Chiang, Weiwei Duan, Stefan Leyk, Johannes H. Uhl, Craig A. Knoblock
Series Title: SpringerBriefs in Geography
DOI: https://doi.org/10.1007/978-3-319-66908-3
Publisher: Springer Cham
eBook Packages: Earth and Environmental Science, Earth and Environmental Science (R0)
Copyright Information: The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Softcover ISBN: 978-3-319-66907-6Published: 26 November 2019
eBook ISBN: 978-3-319-66908-3Published: 17 November 2019
Series ISSN: 2211-4165
Series E-ISSN: 2211-4173
Edition Number: 1
Number of Pages: X, 114
Number of Illustrations: 1 b/w illustrations, 75 illustrations in colour
Topics: Geographical Information Systems/Cartography, Physical Geography, Image Processing and Computer Vision