Building Linked Spatio-Temporal Data from Vectorized Historical Maps

Basel Shbita; Craig A. Knoblock; Weiwei Duan; Yao Yi Chiang; Johannes H. Uhl; Stefan Leyk

doi:10.1007/978-3-030-49461-2_24

Building Linked Spatio-Temporal Data from Vectorized Historical Maps

Basel Shbita, Craig A. Knoblock, Weiwei Duan, Yao Yi Chiang, Johannes H. Uhl, Stefan Leyk

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Scopus citations

Abstract

Historical maps provide a rich source of information for researchers in the social and natural sciences. These maps contain detailed documentation of a wide variety of natural and human-made features and their changes over time, such as the changes in the transportation networks and the decline of wetlands. It can be labor-intensive for a scientist to analyze changes across space and time in such maps, even after they have been digitized and converted to a vector format. In this paper, we present an unsupervised approach that converts vector data of geographic features extracted from multiple historical maps into linked spatio-temporal data. The resulting graphs can be easily queried and visualized to understand the changes in specific regions over time. We evaluate our technique on railroad network data extracted from USGS historical topographic maps for several regions over multiple map sheets and demonstrate how the automatically constructed linked geospatial data enables effective querying of the changes over different time periods.

Original language	English (US)
Title of host publication	The Semantic Web - 17th International Conference, ESWC 2020, Proceedings
Editors	Andreas Harth, Sabrina Kirrane, Axel-Cyrille Ngonga Ngomo, Heiko Paulheim, Anisa Rula, Anna Lisa Gentile, Peter Haase, Michael Cochez
Publisher	Springer
Pages	409-426
Number of pages	18
ISBN (Print)	9783030494605
DOIs	https://doi.org/10.1007/978-3-030-49461-2_24
State	Published - 2020
Externally published	Yes
Event	17th Extended Semantic Web Conference, ESWC 2020 - Heraklion, Greece Duration: May 31 2020 → Jun 4 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12123 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th Extended Semantic Web Conference, ESWC 2020
Country/Territory	Greece
City	Heraklion
Period	5/31/20 → 6/4/20

Bibliographical note

Funding Information:
Acknowledgements. This material is based upon work supported by the National Science Foundation under Grant Nos. IIS 1564164 (to the University of Southern California) and IIS 1563933 (to the University of Colorado at Boulder).

Funding Information:
This material is based upon work supported by the National Science Foundation under Grant Nos. IIS 1564164 (to the University of Southern California) and IIS 1563933 (to the University of Colorado at Boulder).

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Keywords

Historical maps
Knowledge graphs
Linked spatio-temporal data
Semantic web

Publisher link

10.1007/978-3-030-49461-2_24

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Cite this

Shbita, B., Knoblock, C. A., Duan, W., Chiang, Y. Y., Uhl, J. H., & Leyk, S. (2020). Building Linked Spatio-Temporal Data from Vectorized Historical Maps. In A. Harth, S. Kirrane, A.-C. Ngonga Ngomo, H. Paulheim, A. Rula, A. L. Gentile, P. Haase, & M. Cochez (Eds.), The Semantic Web - 17th International Conference, ESWC 2020, Proceedings (pp. 409-426). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12123 LNCS). Springer. https://doi.org/10.1007/978-3-030-49461-2_24

Building Linked Spatio-Temporal Data from Vectorized Historical Maps. / Shbita, Basel; Knoblock, Craig A.; Duan, Weiwei et al.
The Semantic Web - 17th International Conference, ESWC 2020, Proceedings. ed. / Andreas Harth; Sabrina Kirrane; Axel-Cyrille Ngonga Ngomo; Heiko Paulheim; Anisa Rula; Anna Lisa Gentile; Peter Haase; Michael Cochez. Springer, 2020. p. 409-426 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12123 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Shbita, B, Knoblock, CA, Duan, W, Chiang, YY, Uhl, JH & Leyk, S 2020, Building Linked Spatio-Temporal Data from Vectorized Historical Maps. in A Harth, S Kirrane, A-C Ngonga Ngomo, H Paulheim, A Rula, AL Gentile, P Haase & M Cochez (eds), The Semantic Web - 17th International Conference, ESWC 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12123 LNCS, Springer, pp. 409-426, 17th Extended Semantic Web Conference, ESWC 2020, Heraklion, Greece, 5/31/20. https://doi.org/10.1007/978-3-030-49461-2_24

Shbita B, Knoblock CA, Duan W, Chiang YY, Uhl JH, Leyk S. Building Linked Spatio-Temporal Data from Vectorized Historical Maps. In Harth A, Kirrane S, Ngonga Ngomo AC, Paulheim H, Rula A, Gentile AL, Haase P, Cochez M, editors, The Semantic Web - 17th International Conference, ESWC 2020, Proceedings. Springer. 2020. p. 409-426. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-49461-2_24

Shbita, Basel ; Knoblock, Craig A. ; Duan, Weiwei et al. / Building Linked Spatio-Temporal Data from Vectorized Historical Maps. The Semantic Web - 17th International Conference, ESWC 2020, Proceedings. editor / Andreas Harth ; Sabrina Kirrane ; Axel-Cyrille Ngonga Ngomo ; Heiko Paulheim ; Anisa Rula ; Anna Lisa Gentile ; Peter Haase ; Michael Cochez. Springer, 2020. pp. 409-426 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Building Linked Spatio-Temporal Data from Vectorized Historical Maps

Abstract

Publication series

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Bibliographical note

Keywords

Publisher link

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