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on1128180174 |
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OCoLC |
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20240909213021.0 |
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191121s2020 sz e o 0 eng d |
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|a 10.1007/978-3-319-66908-3
|2 doi
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|a 10.1007/978-3-319-66
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|a com.springer.onix.9783319669083
|b Springer Nature
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|a G1030
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| 049 |
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|a HCDD
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| 100 |
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|a Chiang, Yao-Yi,
|e author.
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| 245 |
1 |
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|a Using historical maps in scientific studies :
|b applications, challenges, and best practices /
|c Yao-Yi Chiang, Weiwei Duan, Stefan Leyk, Johannes H. Uhl, Craig A. Knoblock.
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| 264 |
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1 |
|a Cham, Switzerland :
|b Springer,
|c [2020]
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| 300 |
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|a 1 online resource (x, 114 pages) :
|b illustrations (some color)
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| 336 |
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|a cartographic image
|b cri
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a SpringerBriefs in geography,
|x 2211-4165
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| 504 |
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|a Includes bibliographical references.
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|a Online resource; title from PDF title page (SpringerLink, viewed November 21, 2019).
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|a Intro; Acknowledgments; Contents; 1 Introduction; 1.1 Book Objectives; 1.2 Book Structure; References; 2 Historical Map Applications and Processing Technologies; 2.1 Introduction; 2.2 Applications of Historical Maps; 2.3 Case Studies of Map Processing Technologies; 2.3.1 Case Study I: Semi-Automatic Symbol Recognition from Map Scans; 2.3.1.1 SURF (Speeded Up Robust Features) Matching; 2.3.1.2 Histogram Matching; 2.3.1.3 Result Consolidation; 2.3.1.4 Results and Discussion; 2.3.2 Case Study II: Multi-Model, Context-Based Automatic Symbol Recognition from Map Scans
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|a 2.3.2.1 Graphics Sampling Using Contextual Information2.3.2.2 Results and Discussion; 2.3.3 Case Study Discussion and Outlook; 2.4 Chapter Summary; References; 3 Creating Structured, Linked Geographic Data from Historical Maps: Challenges and Trends; 3.1 Introduction; 3.2 Finding Relevant Historical Maps; 3.3 Converting Map Content to Machine-Readable Formats and Record Uncertainty; 3.3.1 Crowdsourcing Approaches; 3.3.2 Semi-automatic Approaches; 3.3.3 Multi-Model, Context-Based, Automatic Approaches; 3.4 Modeling and Publishing Map Content; 3.5 Chapter Summary; References
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|a 4 Training Deep Learning Models for Geographic Feature Recognition from Historical Maps4.1 Introduction; 4.2 Challenges in Using CNNs on Historical Maps; 4.2.1 Accurate Boundary Delineation of Geographic Features; 4.2.2 Scarce Training Data for Cartographic Documents; 4.3 Overview of Semantic Segmentation for Geographic Feature Recognition from Map Scans; 4.3.1 VGG16: The 16-layer Very Deep Convolutional Networks for Large-Scale Image Recognition; 4.3.2 GoogLeNet; 4.3.3 ResNet; 4.3.4 The Encoder and Decoder Architecture for Semantic Segmentation
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|a 4.3.5 Multi-Scale Pyramids of Feature Images for Semantic Segmentation4.4 Overview of Transfer Learning for Geographic Feature Recognition from Map Scans; 4.5 Experiment; 4.5.1 Experimental Data, Settings and Evaluation Metrics; 4.5.2 Experiment I: The Impact of Backbone CNNs: FCN-VGG16, FCN-GoogLeNet, and FCN-ResNet; 4.5.3 Experiment II: The Impact of Transfer Learning Strategies: PSPNet; 4.5.3.1 Experiment III: Modified PSPNet; 4.6 Chapter Summary; References; 5 Summary and Discussion; 5.1 Book Summary; A Railroad Recognition Results
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|a 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 te chnologies 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!
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| 650 |
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|a Historical geography
|v Maps.
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| 650 |
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7 |
|a historical maps.
|2 aat
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| 650 |
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|a Historical geography
|2 fast
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| 655 |
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|a atlases.
|2 aat
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|a Atlases
|2 fast
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|a Maps
|2 fast
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| 655 |
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|a Atlases.
|2 lcgft
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| 655 |
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|a Maps.
|2 lcgft
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|a Atlas.
|2 rvmgf
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|a Cartes géographiques.
|2 rvmgf
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| 700 |
1 |
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|a Duan, Weiwei,
|e author.
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| 700 |
1 |
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|a Leyk, Stefan,
|e author.
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| 700 |
1 |
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|a Uhl, Johannes H.,
|e author.
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| 700 |
1 |
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|a Knoblock, Craig A.,
|d 1962-
|e author.
|1 https://id.oclc.org/worldcat/entity/E39PBJh4BpB6JFcqCtW6g3FkDq
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| 758 |
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|i has work:
|a Using historical maps in scientific studies (CartographicMaterial)
|1 https://id.oclc.org/worldcat/entity/E39PCH73wp6yCcQmhBFGbhKx8P
|4 https://id.oclc.org/worldcat/ontology/hasWork
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| 830 |
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|a SpringerBriefs in geography,
|x 2211-4165
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| 856 |
4 |
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|u https://holycross.idm.oclc.org/login?auth=cas&url=https://link.springer.com/10.1007/978-3-319-66908-3
|y Click for online access
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| 903 |
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|a SPRING-EARTH2020
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| 994 |
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|a 92
|b HCD
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