Towards a foundation model for geospatial artificial intelligence (vision paper)-Reference-Cited by-同舟云学术

Towards a foundation model for geospatial artificial intelligence (vision paper)

Published:2022-11 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 30th International Conference on Advances in Geographic Information Systems
language:
Short-container-title:

Author:

Mai Gengchen¹,Cundy Chris¹,Choi Kristy¹,Hu Yingjie²,Lao Ni³,Ermon Stefano¹

Affiliation:

1. Stanford University

2. University at Buffalo

3. Google

Funder

Sloan Fellowship

Qualcomm Innovation Fellowship

Two Sigma PhD Diversity Fellowship

Army Research Office

CZ Biohub

Amazon AWS

Air Force Office of Scientific Research

Office of Naval Research

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3557915.3561043

Reference27 articles.

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2. Adapting the Edinburgh geoparser for historical geo-referencing;Beatrice Alex;International Journal of Humanities and Arts Computing,2015

3. Kumar Ayush et al. 2021. Geography-aware self-supervised learning . In CVPR 2021 . 10181--10190. Kumar Ayush et al. 2021. Geography-aware self-supervised learning. In CVPR 2021. 10181--10190.

4. Rishi Bommasani et al. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021). Rishi Bommasani et al. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021).

5. Tom Brown et al. 2020. Language models are few-shot learners . NIPS 2020 33 (2020) , 1877--1901. Tom Brown et al. 2020. Language models are few-shot learners. NIPS 2020 33 (2020), 1877--1901.

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