Table Of ContentSPRINGER BRIEFS IN GEOGRAPHY
Yao-Yi Chiang · Weiwei Duan
Stefan Leyk · Johannes H. Uhl
Craig A. Knoblock
Using Historical
Maps in
Scientific Studies
Applications,
Challenges, and
Best Practices
123
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Yao-Yi Chiang • Weiwei Duan • Stefan Leyk
Johannes H. Uhl • Craig A. Knoblock
Using Historical Maps
in Scientific Studies
Applications, Challenges, and Best Practices
Yao-YiChiang WeiweiDuan
SpatialSciencesInstitute DepartmentofComputerScience
UniversityofSouthernCalifornia UniversityofSouthernCalifornia
LosAngeles,CA,USA LosAngeles,CA,USA
StefanLeyk JohannesH.Uhl
DepartmentofGeography DepartmentofGeography
UniversityofColorado UniversityofColorado
Boulder,CO,USA Boulder,CO,USA
CraigA.Knoblock
InformationSciencesInstitute
UniversityofSouthernCalifornia
MarinadelRey,CA,USA
ISSN2211-4165 ISSN2211-4173 (electronic)
SpringerBriefsinGeography
ISBN978-3-319-66907-6 ISBN978-3-319-66908-3 (eBook)
https://doi.org/10.1007/978-3-319-66908-3
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ForBaobao
Acknowledgments
TheauthorsthankHayleySong,ZekunLi,andYijunLin.
This material is based on research supported in part by the National Science
FoundationunderGrantNo.IIS1563933(totheUniversityofColoradoatBoulder)
andIIS1564164(totheUniversityofSouthernCalifornia),inpartbytheNational
Endowment for the Humanities (Grant No. NEH PR-253386-17), in part by the
University of Southern California Undergraduate Research Associates Program
(USCURAP),andinpartbyMicrosoftandNVIDIACorporation.
vii
Contents
1 Introduction .................................................................. 1
1.1 BookObjectives......................................................... 1
1.2 BookStructure .......................................................... 7
References..................................................................... 7
2 HistoricalMapApplicationsandProcessingTechnologies.............. 9
2.1 Introduction ............................................................. 10
2.2 ApplicationsofHistoricalMaps........................................ 13
2.3 CaseStudiesofMapProcessingTechnologies........................ 19
2.3.1 CaseStudyI:Semi-AutomaticSymbolRecognition
fromMapScans................................................. 21
2.3.2 CaseStudyII:Multi-Model,Context-BasedAutomatic
SymbolRecognitionfromMapScans......................... 29
2.3.3 CaseStudyDiscussionandOutlook........................... 32
2.4 ChapterSummary....................................................... 33
References..................................................................... 34
3 CreatingStructured,LinkedGeographicDatafromHistorical
Maps:ChallengesandTrends.............................................. 37
3.1 Introduction ............................................................. 37
3.2 FindingRelevantHistoricalMaps...................................... 43
3.3 ConvertingMapContenttoMachine-ReadableFormatsand
RecordUncertainty ..................................................... 46
3.3.1 CrowdsourcingApproaches.................................... 47
3.3.2 Semi-automaticApproaches ................................... 49
3.3.3 Multi-Model,Context-Based,AutomaticApproaches........ 51
3.4 ModelingandPublishingMapContent................................ 55
3.5 ChapterSummary....................................................... 59
References..................................................................... 60
ix
x Contents
4 Training Deep Learning Models for Geographic Feature
RecognitionfromHistoricalMaps......................................... 65
4.1 Introduction ............................................................. 66
4.2 ChallengesinUsingCNNsonHistoricalMaps....................... 67
4.2.1 AccurateBoundaryDelineationofGeographicFeatures..... 69
4.2.2 ScarceTrainingDataforCartographicDocuments........... 70
4.3 OverviewofSemanticSegmentationforGeographicFeature
RecognitionfromMapScans........................................... 74
4.3.1 VGG16: The 16-layer Very Deep Convolutional
NetworksforLarge-ScaleImageRecognition ................ 75
4.3.2 GoogLeNet...................................................... 77
4.3.3 ResNet........................................................... 78
4.3.4 TheEncoderandDecoderArchitectureforSemantic
Segmentation.................................................... 79
4.3.5 Multi-ScalePyramidsofFeatureImagesforSemantic
Segmentation.................................................... 82
4.4 Overview of Transfer Learning for Geographic Feature
RecognitionfromMapScans........................................... 84
4.5 Experiment .............................................................. 85
4.5.1 ExperimentalData,SettingsandEvaluationMetrics ......... 86
4.5.2 Experiment I: The Impact of Backbone CNNs:
FCN-VGG16,FCN-GoogLeNet,andFCN-ResNet .......... 88
4.5.3 Experiment II: The Impact of Transfer Learning
Strategies:PSPNet.............................................. 89
4.6 ChapterSummary....................................................... 96
References..................................................................... 96
5 SummaryandDiscussion ................................................... 99
5.1 BookSummary.......................................................... 99
A RailroadRecognitionResults............................................... 101
Chapter 1
Introduction
Abstract Historical maps are fascinating to look at and contain valuable retro-
spectiveplaceinformationdifficulttofindelsewhere.However,thefullpotentialof
historicalmapshasnotbeenrealizedbecausetheusersofscannedhistoricalmaps
andthedevelopersofdigitalmapprocessingtechnologiesarefromawiderangeof
disciplines and often work in silos. This book aims to make the first connection
between the map user community and the developers of digital map processing
technologies by illustrating several applications, challenges, and best practices in
workingwithhistoricalmaps.Thischapterpresentsabriefintroductiontovarious
typesofhistoricalmapsandthescientificstudiesthatcouldbenefitfromusingthem.
Further, the chapter summarizes the general considerations critical for building
successful computational processes that can be used to analyze historical map
content.Finally,thechapterprovidesanoverviewofthebookstructure,describing
theconnectionsbetweenindividualchapters.
1.1 BookObjectives
Ever since the prehistorical times, various forms of maps represent an important
medium for visual communication about human-made elements, human activities,
andnaturalphenomenaonEarth(orinthesky)aswellastheirspatialrelationships.1
Between cave paintings to Google Maps, the idea of mapmaking has gone from
flat Earth to numerous spatial reference systems, and the purpose of maps has
progressed from storytelling to navigation. During the period of hundreds and
thousandsofyears,mapmakers(cartographers)haveproducedasignificantamount
of maps. For example, in 1502, Leonardo da Vinci created the Imola town plan
(Fig.1.1) [BF07], which is arguably the first scientifically measured map (or
ichnographic)[BL14].Althoughproducedwithsimplemeasuringtoolsbytoday’s
standard, the da Vinci’s Imola map still mostly matches the current street layout
1Thereaderisreferredtothemulti-volumebook[HWL87]foragrandoverviewofmapmaking
(cartography)fromprehistoricaltimestothetwentiethcentury.
©TheAuthor(s),underexclusivelicensetoSpringerNatureSwitzerlandAG2020 1
Y.-Y.Chiangetal.,UsingHistoricalMapsinScientificStudies,SpringerBriefs
inGeography,https://doi.org/10.1007/978-3-319-66908-3_1
