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Creating Structured, Linked Geographic Data from Historical Maps: Challenges and Trends

  • Yao-Yi Chiang
  • Weiwei Duan
  • Stefan Leyk
  • Johannes H. Uhl
  • Craig A. Knoblock
Chapter
Part of the SpringerBriefs in Geography book series (BRIEFSGEOGRAPHY)

Abstract

Historical geographic data are essential for a variety of studies of cancer and environmental epidemiology, urbanization, and landscape ecology. However, existing data sources typically contain only contemporary information. Historical maps hold a great deal of detailed geographic information at various times in the past. Yet, finding relevant maps is difficult, and the map content is not machine-readable. This chapter presents the 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). This framework will allow making use of historical geographic datasets from a variety of maps, efficiently, over large geographic extents. Realizing such a framework poses significant research challenges in multiple fields in computer science including digital map processing, data integration, and the Semantic Web technologies, and other disciplines such as spatial, social, and health sciences. Tackling these challenges will not only advance research in computer science and geographic information science but also present a unique opportunity for interdisciplinary research.

References

  1. [Art13]
    M.G. Arteaga, Historical map polygon and feature extractor, in MapInteract 2013, Proceedings of the 1st ACM SIGSPATIAL International Workshop on MapInteraction, November 5th, 2013, Orlando, Florida, USA (2013), pp. 66–71. https://doi.org/10.1145/2534931.2534932
  2. [Bar+18]
    B. Barz, T.C. van Dijk, B. Spaan, J. Denzler, Putting user reputation on the map: unsupervised quality control for crowdsourced historical data, in Proceedings of the 2nd ACM SIGSPATIAL Workshop on Geospatial Humanities, GeoHumanities’18 (ACM, New York, 2018), pp. 3:1–3:6. ISBN: 978-1-4503-6032-6. https://doi.org/10.1145/3282933.3282937
  3. [Bas+18]
    F. Bastani, S. He, S. Abbar, M. Alizadeh, H. Balakrishnan, S. Chawla, S. Madden, Machine-assisted map editing, in Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL ’18 (ACM, New York, 2018), pp. 23–32. ISBN: 978-1-4503-5889-7. https://doi.org/10.1145/3274895.3274927 CrossRefGoogle Scholar
  4. [BD15]
    B. Budig, T.C. van Dijk, Active learning for classifying template matches in historical maps, in Discovery Science, ed. by N. Japkowicz, S. Matwin. Lecture Notes in Computer Science (Springer, Berlin, 2015), pp. 33–47. ISBN: 9783319242811, 9783319242828. https://doi.org/10.1007/978-3-319-24282-8_5 CrossRefGoogle Scholar
  5. [BDW16]
    B. Budig, T.C.V. Dijk, A. Wolff, Matching labels and markers in historical maps: an algorithm with interactive postprocessing, in ACM Transactions on Spatial Algorithms and Systems (TSAS) 2.4 (2016), pp. 13:1–13:24. ISSN: 2374-0353. https://doi.org/10.1145/2994598 CrossRefGoogle Scholar
  6. [Bea14]
    C.S. Beattie, 3D visualization models as a tool for reconstructing the historical landscape of the Ballona creek watershed, MA thesis, University of Southern California, 2014Google Scholar
  7. [BHB09]
    C. Bizer, T. Heath, T. Berners-Lee, Linked data - the story so far. Int. J. Semant. Web Inf. Syst. 5(3), 1–22 (2009). ISSN: 1552-6283CrossRefGoogle Scholar
  8. [Bud+16]
    B. Budig, T.C. van Dijk, F. Feitsch, M.G. Arteaga, Polygon consensus: smart crowdsourcing for extracting building footprints from historical maps, in Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPACIAL ’16 (ACM, New York, 2016), pp. 66:1–66:4. ISBN: 978-1-4503-4589-7. https://doi.org/10.1145/2996913.2996951
  9. [Bud16]
    B. Budig, Efficient algorithms and user interaction for metadata extraction from historical maps, in Proceedings of the 2Nd ACM SIGSPATIAL PhD Workshop, SIGSPATIAL PhD ’15 (ACM, New York, 2016), pp. 4:1–4:4. ISBN: 978-1-4503-3980-3. https://doi.org/10.1145/2855680.2855841
  10. [Bud18]
    B. Budig, Extracting spatial information from historical maps: algorithms and interaction, PhD thesis, University of Würzburg, 2018Google Scholar
  11. [BvK16]
    B. Budig, T.C. van Dijk, F. Kirchner, Glyph miner: a system for efficiently extracting glyphs from early prints in the context of OCR, in 2016 IEEE/ACM Joint Conference on Digital Libraries (JCDL) (2016), pp. 31–34Google Scholar
  12. [CCM14]
    Y.-Y. Chiang, P. Chioh, S. Moghaddam, A training-by-example approach for symbol spotting from raster maps, in Proceedings of the 8th International Conference on Geographic Information Science (2014), pp. 264–269Google Scholar
  13. [Che+04]
    C.-C. Chen, C.A. Knoblock, C. Shahabi, Y.-Y. Chiang, S. Thakkar, Automatically and accurately conflating orthoimagery and street maps, in Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems, GIS ’04 (ACM, New York, 2004), pp. 47–56. ISBN: 9781581139792. https://doi.org/10.1145/1032222.1032231
  14. [Chi+14]
    Y.-Y. Chiang, S. Moghaddam, S. Gupta, R. Fernandes, C.A. Knoblock, From map images to geographic names, in Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM, New York, 2014), pp. 581–584. ISBN: 9781450331319. https://doi.org/10.1145/2666310.2666374
  15. [Chi+15]
    Y.-Y. Chiang, S. Leyk, N.H. Nazari, S. Moghaddam, The impact of graphical quality on automatic text recognition in digital maps, in Proceedings of the 27th International Cartographic Conference (2015) ISBN: 9788588783119Google Scholar
  16. [Chi+16]
    Y.-Y. Chiang, S. Leyk, N.H. Nazari, S. Moghaddam, T.X. Tan, Assessing the impact of graphical quality on automatic text recognition in digital maps. Comput. Geosci. 93, 21–35 (2016). ISSN: 0098-3004. https://doi.org/10.1016/j.cageo.2016.04.013 CrossRefGoogle Scholar
  17. [Chi10]
    Y.-Y. Chiang, Harvesting geographic features from heterogeneous raster maps, PhD thesis, Los Angeles, CA, USA: University of Southern California, 2010. ISBN: 9781124412498Google Scholar
  18. [Chi13]
    Y.-Y. Chiang, Strabo: a complete system for label recognition in maps, in Proceedings of the 26th International Cartographic Conference (ICC’13) (2013), pp. 838–838. ISBN: 9781907075063Google Scholar
  19. [Chi15]
    Y.-Y. Chiang, Querying historical maps as a unified, structured, and linked spatiotemporal source: vision paper, in Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. GIS ’15 (ACM, New York, 2015), pp. 16:1–16:4. ISBN: 9781450339674. https://doi.org/10.1145/2820783.2820887
  20. [Chi17]
    Y.-Y. Chiang, Unlocking textual content from historical maps - potentials and applications, trends, and outlooks, in Recent Trends in Image Processing and Pattern Recognition, ed. by K. Santosh, M. Hangarge, V. Bevilacqua, A. Negi (Springer, Singapore, 2017), pp. 111–124. ISBN: 978-981-10-4859-3CrossRefGoogle Scholar
  21. [CK08]
    Y.-Y. Chiang, C.A. Knoblock, Automatic extraction of road intersection position, connectivity, and orientations from raster maps, in Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM Press, New York, 2008), pp. 1–10. ISBN: 9781605583235. https://doi.org/10.1145/1463434.1463463
  22. [CK14]
    Y.-Y. Chiang, C.A. Knoblock, Recognizing text in raster maps. GeoInformatica 19(1), 1–27 (2014). ISSN: 1384-6175, 1573-7624. https://doi.org/10.1007/s10707-014-0203-9 CrossRefGoogle Scholar
  23. [CL15]
    Y.-Y. Chiang, S. Leyk, Exploiting online gazetteer for fully automatic extraction of cartographic symbols, in Proceedings of the 27th International Cartographic Conference (2015). ISBN: 9788588783119Google Scholar
  24. [Cla10]
    K.C. Clarke, Getting Started with Geographic Information Systems (Pearson, London, 2010). ISBN: 9780131494985Google Scholar
  25. [CLK13]
    Y.-Y. Chiang, S. Leyk, C.A. Knoblock, Efficient and robust graphics recognition from historical maps, in Graphics Recognition. New Trends and Challenges: 9th International Workshop, GREC 2011, Seoul, Korea, September 15–16, 2011, Revised Selected Papers, vol. 7423, ed. by Y.-B. Kwon, J.-M. Ogier. Lecture Notes in Computer Science, GREC’11 (Springer, Berlin, 2013), pp. 25–35. ISBN: 9783642368233. https://doi.org/10.1007/978-3-642-36824-0_3 CrossRefGoogle Scholar
  26. [CLK14]
    Y.-Y. Chiang, S. Leyk, C.A. Knoblock, A survey of digital map processing techniques, in ACM Comput. Surv. 47(1), 1–44 (2014). ISSN: 0360-0300. https://doi.org/10.1145/2557423 CrossRefGoogle Scholar
  27. [Dua+17]
    W. Duan, Y.-Y. Chiang, C.A. Knoblock, V. Jain, D. Feldman, J.H. Uhl, S. Leyk, Automatic alignment of geographic features in contemporary vector data and historical maps, in Proceedings of the 1st Workshop on Artificial Intelligence and Deep Learning for Geographic Knowledge Discovery (ACM, New York, 2017), pp. 45–54Google Scholar
  28. [Dua+18]
    W. Duan, Y. Chiang, C.A. Knoblock, S. Leyk, J. Uhl, Automatic generation of precisely delineated geographic features from georeferenced historical maps using deep learning, in Proceedings of the AutoCarto (2018)Google Scholar
  29. [ESR98]
    ESRI, ESRI shapefile technical description, Tech. rep. ESRI, 1998Google Scholar
  30. [FC97]
    S. Frischknecht, A. Carosio, Raster-based methods to extract structured information from scanned topographic maps, in International Archives of Photogrammetry and Remote Sensing, vol. 32, Part 3-4W2 (1997), pp. 1–5Google Scholar
  31. [FK97]
    S. Frischknecht, E. Kanani, Automatic interpretation of scanned topographic maps: a raster-based approach, in Graphics Recognition Algorithms and Systems, vol. 1398 (Springer, Berlin, 1997), pp. 207–220. ISBN: 9783540643814. https://doi.org/10.1007/3-540-64381-8_50 CrossRefGoogle Scholar
  32. [GE07]
    I.N. Gregory, P.S. Ell, Historical GIS: Technologies, Methodologies, and Scholarship, vol. 39. Cambridge Studies in Historical Geography (Cambridge University Press, Cambridge, 2007). ISBN: 9781139467711Google Scholar
  33. [GE15]
    B. Godfrey, H. Eveleth, An adaptable approach for generating vector features from scanned historical thematic maps using image enhancement and remote sensing techniques in a in a geographic information system. J. Map Geogr. Libr., 18–36 (2015). ISSN: 1542-0353. https://doi.org/10.1080/15420353.2014.1001107 CrossRefGoogle Scholar
  34. [Gel08]
    J. Gelernter, MapSearch: a protocol and prototype application to find maps, PhD thesis. Rutgers, The State University of New Jersey, 2008Google Scholar
  35. [GGH15]
    D. Garijo, Y. Gil, A. Harth, Challenges for provenance analytics over geospatial data, in Provenance and Annotation of Data and Processes, vol. 8628, ed. by B. Ludäscher, B. Plale. Lecture Notes in Computer Science (Springer, Berlin, 2015), pp. 261–263Google Scholar
  36. [GS62]
    C.R. Greenwalt, M.E. Shultz, Principles of error theory and cartographic applications, Tech. rep. 1962Google Scholar
  37. [Jan+12]
    K. Janowicz, S. Scheider, T. Pehle, G. Hart, Geospatial semantics and linked spatiotemporal data – past, present, and future. Semantic Web 3(4), 321–332 (2012). https://doi.org/10.3233/SW-2012-0077 Google Scholar
  38. [Kur13]
    L. Kurashige, Rethinking anti-immigrant racism: lessons from the los angeles vote on the 1920 Alien land law. South. Calif. Q. 95(3), 265–283 (2013). ISSN: 0038-3929.  https://doi.org/10.1525/scq.2013.95.3.265 CrossRefGoogle Scholar
  39. [KZ03]
    A. Khotanzad, E. Zink, Contour line and geographic feature extraction from USGS color topographical paper maps. IEEE Trans. Pattern Anal. Mach. Intell. 25(1), 18–31 (2003). Issn: 0162-8828.  https://doi.org/10.1109/TPAMI.2003.1159943 CrossRefGoogle Scholar
  40. [LBW05]
    S. Leyk, R. Boesch, R. Weibel, A conceptual framework for uncertainty investigation in map-based land cover change modelling. Trans. GIS 9(3), 291–322 (2005). https://doi.org/10.1111/j.1467-9671.2005.00220.x eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1467-9671.2005.00220.x CrossRefGoogle Scholar
  41. [LC18a]
    H. Lin, Y.-Y. Chiang, An uncertainty aware method for geographic data conflation, in Proceedings of the 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2018 (ACM, Seattle, 2018), pp. 20–27. ISBN: 978-1-4503-6041-8. https://doi.org/10.1145/3282834.3282842 CrossRefGoogle Scholar
  42. [LC18b]
    H. Lin, Y.-Y. Chiang, SRC: automatic extraction of phrase-level map labels from historical maps. SIGSPATIAL Spec. 9(3), 14–15 (2018)CrossRefGoogle Scholar
  43. [LLZ18]
    H. Li, J. Liu, X. Zhou, Intelligent map reader: a framework for topographic map understanding with deep learning and gazetteer. IEEE Access 6, 25363–25376 (2018). ISSN: 2169-3536.  https://doi.org/10.1109/ACCESS.2018.2823501 CrossRefGoogle Scholar
  44. [LSD15]
    J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 3431–3440Google Scholar
  45. [Man+17]
    S. Manson, J. Schroeder, D. Van Riper, S. Ruggles, et al., IPUMS National Historical Geographic Information System: Version 12.0 [Database], Minneapolis: University of Minnesota (2017)Google Scholar
  46. [Mit77]
    W.B. Mitchell, GIRAS: a geographic information retrieval and analysis system for handling land use and land cover data, 1059. US Govt. Print. Off. (1977)Google Scholar
  47. [Nag+97]
    G. Nagy, A. Samal, S. Seth, T. Fisher, et al., Reading street names from maps-technical challenges, in Proceedings of GIS/LIS (1997)Google Scholar
  48. [Pag+99]
    L. Page, S. Brin, R. Motwani, T. Winograd, The PageRank citation ranking: bringing order to the web, Tech. rep. Stanford InfoLab, 1999Google Scholar
  49. [Pez11]
    A. Pezeshk, Feature extraction and text recognition from scanned color topographic maps, PhD thesis, Pennsylvania State University, 2011Google Scholar
  50. [S+15]
    R. Simon, E. Barker, L. Isaksen, et al., Linking early geospatial documents, one place at a time: annotation of geographic documents with recogito, in e- (2015). http://oro.open.ac.uk/43613/ Google Scholar
  51. [Sim+10]
    R. Simon, C. Sadilek, J. Korb, M. Baldauf, B. Haslhofer, Tag clouds and old maps: annotations as linked spatiotemporal data in the cultural heritage domain, in Workshop On Linked Spatiotemporal Data, Zurich, Switzerland (2010)Google Scholar
  52. [Sim+14]
    R. Simon, P. Pilgerstorfer, L. Isaksen, E. Barker, Towards semi-automatic annotation of toponyms on old maps. e - Perimetron 9(3), 105–128 (2014)Google Scholar
  53. [SZ15]
    K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7–9, 2015, Conference Track Proceedings (2015)Google Scholar
  54. [Tam18]
    T. Tambassi, The Philosophy of Geo-Ontologies (Springer, Berlin, 2018)CrossRefGoogle Scholar
  55. [TZ00]
    P.H.S. Torr, A. Zisserman, MLESAC: a new robust estimator with application to estimating image geometry. Comp. Vision Image Underst. 78(1), 138–156 (2000). ISSN: 1077-3142.  https://doi.org/10.1006/cviu.1999.0832 CrossRefGoogle Scholar
  56. [Uhl+17]
    J.H. Uhl, S. Leyk, Y.-Y. Chiang, W. Duan, C.A. Knoblock, Extracting human settlement footprint from historical topographic map series using context-based machine learning, in IET Conference Proceedings (2017)Google Scholar
  57. [Uhl+18a]
    J.H. Uhl, S. Leyk, Y.-Y. Chiang, W. Duan, C.A. Knoblock, Spatialising uncertainty in image segmentation using weakly supervised convolutional neural networks: a case study from historical map processing. IET Image Process. 12(11), 2084–2091 (2018)CrossRefGoogle Scholar
  58. [Uhl+18b]
    J. Uhl, S. Leyk, Y.-Y. Chiang, W. Duan, C. Knoblock, Map archive mining: visual-analytical approaches to explore large historical map collections. ISPRS Int. J. Geo-Inform. 7(4), 148 (2018)CrossRefGoogle Scholar
  59. [Uhl19]
    J.H. Uhl, Spatio-temporal information extraction under uncertainty using multi-source data integration and machine learning: applications to human settlement modelling, PhD thesis, University of Colorado (2019)Google Scholar
  60. [Wei13]
    J. Weinman, Toponym recognition in historical maps by gazetteer alignment, in Proceedings of the 12th International Conference on Document Analysis and Recognition (2013), pp. 1044–1048.  https://doi.org/10.1109/ICDAR.2013.209
  61. [Wei17]
    J. Weinman, Geographic and style models for historical map alignment and toponym recognition, in 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1 (IEEE, Piscataway, 2017), pp. 957–964Google Scholar
  62. [YLC16]
    R. Yu, Z. Luo, Y.-Y. Chiang, Recognizing text in historical maps using maps from multiple time periods, in 2016 23rd International Conference on Pattern Recognition (ICPR) (IEEE, Piscataway, 2016), pp. 3993–3998Google Scholar
  63. [Zha+17]
    H. Zhao, J. Shi, X. Qi, X. Wang, J. Jia, Pyramid scene parsing network, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 2881–2890Google Scholar

Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yao-Yi Chiang
    • 1
  • Weiwei Duan
    • 2
  • Stefan Leyk
    • 3
  • Johannes H. Uhl
    • 3
  • Craig A. Knoblock
    • 4
  1. 1.Spatial Sciences InstituteUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Department of Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA
  3. 3.Department of GeographyUniversity of ColoradoBoulderUSA
  4. 4.Information Sciences InstituteUniversity of Southern CaliforniaMarina del ReyUSA

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