Symbolic and subsymbolic<scp>GeoAI</scp>: Geospatial knowledge graphs and spatially explicit machine learning-Reference-Cited by-同舟云学术

Symbolic and subsymbolicGeoAI: Geospatial knowledge graphs and spatially explicit machine learning

Published:2022-12 Issue:8 Volume:26 Page:3118-3124
ISSN:1361-1682
Container-title:Transactions in GIS
language:en
Short-container-title:Transactions in GIS

Author:

Mai Gengchen¹^ORCID,Hu Yingjie²^ORCID,Gao Song³^ORCID,Cai Ling⁴^ORCID,Martins Bruno⁵^ORCID,Scholz Johannes⁶^ORCID,Gao Jing⁷^ORCID,Janowicz Krzysztof⁴⁸

Affiliation:

1. Spatially Explicit Artificial Intelligence Lab, Department of Geography University of Georgia Athens Georgia USA

2. GeoAI Lab, Department of Geography University at Buffalo Buffalo New York USA

3. GeoDS Lab, Department of Geography University of Wisconsin‐Madison Madison Wisconsin USA

4. Center for Spatial Studies University of California, Santa Barbara Santa Barbara California USA

5. Instituto Superior Técnico and INESC‐ID University of Lisbon Lisbon Portugal

6. Institute of Geodesy Graz University of Technology Graz Austria

7. Department of Geography and Spatial Sciences University of Delaware Newark Delaware USA

8. Department of Geography and Regional Research University of Vienna Vienna Austria

Publisher

Wiley

Subject

General Earth and Planetary Sciences

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/tgis.13012

Reference79 articles.

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2. Battaglia P. W. Hamrick J. B. Bapst V. Sanchez‐Gonzalez A. Zambaldi V. Malinowski M. Tacchetti A. Raposo D. Santoro A. Faulkner R. Gulcehre C. Song F. Ballard A. Gilmer J. Dahl G. Vaswani A. Allen K. Nash C. Langston V. …Pascanu R.(2018).Relational inductive biases deep learning and graph networks. arXiv:1806.01261.

3. Enabling the geospatial Semantic Web with Parliament and GeoSPARQL

4. An ontology-based algorithm for managing the evolution of multi-level territorial partitions

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