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|
|Number of pages||18|
|State||Published - 2020|
|Event||17th Extended Semantic Web Conference, ESWC 2020 - Heraklion, Greece|
Duration: May 31 2020 → Jun 4 2020
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||17th Extended Semantic Web Conference, ESWC 2020|
|Period||5/31/20 → 6/4/20|
Bibliographical noteFunding 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).
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).
© Springer Nature Switzerland AG 2020.
- Historical maps
- Knowledge graphs
- Linked spatio-temporal data
- Semantic web