Sphere2Vec: A general-purpose location representation learning over a spherical surface for large-scale geospatial predictions

Author:

Mai GengchenORCID,Xuan YaoORCID,Zuo WenyunORCID,He Yutong,Song Jiaming,Ermon StefanoORCID,Janowicz Krzysztof,Lao Ni

Funder

The Schmidt Family Foundation

Alfred P Sloan Foundation

Office of Research, University of Georgia

Microsoft Corp

Army Research Laboratory

National Science Foundation

University of Georgia

Office of the Director of National Intelligence

Intelligence Advanced Research Projects Activity

Army Research Office

Chan Zuckerberg Initiative

Air Force Office of Scientific Research

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. BB-GeoGPT: A framework for learning a large language model for geographic information science;Information Processing & Management;2024-09

2. Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation;Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval;2024-07-10

3. On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper);ACM Transactions on Spatial Algorithms and Systems;2024-06-30

4. Latent Representation Learning for Geospatial Entities;ACM Transactions on Spatial Algorithms and Systems;2024-05-02

5. SpatialScene2Vec: A self-supervised contrastive representation learning method for spatial scene similarity evaluation;International Journal of Applied Earth Observation and Geoinformation;2024-04

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