This book illustrates the first connection between the map user community and the developers of
digital map processing technologies by providing several applications challenges and best
practices in working with historical maps. After the introduction chapter in this book
Chapter 2 presents a variety of existing applications of historical maps to demonstrate varying
needs for processing historical maps in scientific studies (e.g. thousands of historical maps
from a map series vs. a few historical maps from various publishers and with different
cartographic styles). Chapter 2 also describes case studies introducing typical types of
semi-automatic and automatic digital map processing technologies. 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!
