Wellcome

Using Historical Maps in Scientific Studies (Record no. 549974)

MARC details
000 -LEADER
fixed length control field 05178nam a22005535i 4500
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control field 978-3-319-66908-3
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control field DE-He213
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control field 20211012173850.0
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fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 191117s2020 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783319669083
-- 978-3-319-66908-3
024 7# -
-- 10.1007/978-3-319-66908-3
-- doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
-- GA1-1776
072 #7 -
-- RGW
-- bicssc
-- SCI030000
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-- thema
082 04 -
Classification number 910.285
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100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Chiang, Yao-Yi.
245 10 - TITLE STATEMENT
Title Using Historical Maps in Scientific Studies
Remainder of title Applications, Challenges, and Best Practices /
Statement of responsibility, etc by Yao-Yi Chiang, Weiwei Duan, Stefan Leyk, Johannes H. Uhl, Craig A. Knoblock.
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2020.
300 ## - PHYSICAL DESCRIPTION
Extent X, 114 p. 76 illus., 75 illus. in color.
Other physical details online resource.
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Topical term or geographic name as entry element Geographical information systems.
Topical term or geographic name as entry element Physical geography.
Topical term or geographic name as entry element Optical data processing.
Topical term or geographic name as entry element Geographical Information Systems/Cartography.
Topical term or geographic name as entry element Physical Geography.
Topical term or geographic name as entry element Image Processing and Computer Vision.
700 1# -
Personal name Duan, Weiwei.
Relator term author.
Personal name Leyk, Stefan.
Relator term author.
Personal name Uhl, Johannes H.
Relator term author.
Personal name Knoblock, Craig A.
Relator term author.
710 2# -
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
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Uniform Resource Identifier https://doi.org/10.1007/978-3-319-66908-3
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245 10 - TITLE STATEMENT
-- [electronic resource] :
264 #1 -
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-- Springer International Publishing :
-- Imprint: Springer,
-- 2020.
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-- text
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-- computer
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-- online resource
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-- text file
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490 1# -
-- SpringerBriefs in Geography,
-- 2211-4165
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-- 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!
-- https://scigraph.springernature.com/ontologies/product-market-codes/J13000
-- https://scigraph.springernature.com/ontologies/product-market-codes/J16000
-- https://scigraph.springernature.com/ontologies/product-market-codes/I22021
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-- aut
-- http://id.loc.gov/vocabulary/relators/aut
773 0# -
-- Springer Nature eBook
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-- Printed edition:
-- 9783319669076
-- Printed edition:
-- 9783319669090
830 #0 -
-- SpringerBriefs in Geography,
-- 2211-4165
912 ## -
-- ZDB-2-EES
-- ZDB-2-SXEE
950 ## -
-- Earth and Environmental Science (SpringerNature-11646)
-- Earth and Environmental Science (R0) (SpringerNature-43711)

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