Using historical maps in scientific studies : applications, challenges, and best practices /: applications, challenges, and best practices. ([2020])

• Record Type: Book
• Further Information: Note: Yao-Yi Chiang, Weiwei Duan, Stefan Leyk, Johannes H. Uhl, Craig A. Knoblock.
• Authors: Chiang, Yao-Yi; Duan, Weiwei; Leyk, Stefan; Uhl, Johannes H; Knoblock, Craig A, 1962-
• Contents: 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 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 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 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
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2020
• Extent: 1 online resource (x, 114 pages), illustrations (some color)
• Subjects: 911; Historical geography -- Maps; Electronic books; Electronic books
• Languages: English
• ISBNs: 9783319669083; 3319669087
• Related ISBNs: 9783319669076
• Notes: Note: Includes bibliographical references.; Note: Online resource; title from PDF title page (SpringerLink, viewed November 21, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.