May 8, 2024, 4:46 a.m. | Jin-Jian Xu, Hao Zhang, Chao-Sheng Tang, Lin Li, Bin Shi

cs.CV updates on arXiv.org arxiv.org

arXiv:2311.04940v2 Announce Type: replace
Abstract: As Earth science enters the era of big data, artificial intelligence (AI) not only offers great potential for solving geoscience problems, but also plays a critical role in accelerating the understanding of the complex, interactive, and multiscale processes of Earth's behavior. As geoscience AI models are progressively utilized for significant predictions in crucial situations, geoscience researchers are increasingly demanding their interpretability and versatility. This study proposes an interpretable geoscience artificial intelligence (XGeoS-AI) framework to unravel …

abstract ai models application artificial artificial intelligence arxiv behavior big big data cs.ai cs.cv data earth eess.iv geoscience image image recognition intelligence interactive processes recognition role science type understanding

Doctoral Researcher (m/f/div) in Automated Processing of Bioimages

@ Leibniz Institute for Natural Product Research and Infection Biology (Leibniz-HKI) | Jena

Seeking Developers and Engineers for AI T-Shirt Generator Project

@ Chevon Hicks | Remote

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Senior DevOps Engineer- Autonomous Database

@ Oracle | Reston, VA, United States