Summary of Cracks: Crowdsourcing Resources For Analysis and Categorization Of Key Subsurface Faults, by Mohit Prabhushankar et al.
CRACKS: Crowdsourcing Resources for Analysis and Categorization of Key Subsurface faults
by Mohit Prabhushankar, Kiran Kokilepersaud, Jorge Quesada, Yavuz Yarici, Chen Zhou, Mohammad Alotaibi, Ghassan AlRegib, Ahmad Mustafa, Yusufjon Kumakov
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to detecting and segmenting faults in subsurface imaging data using crowdsourced annotations. The authors leverage Amazon Mechanical Turk to obtain fault delineations from novices, practitioners, and an expert geophysicist, providing benchmarks for detecting and segmenting the expert labels given the novice and practitioner labels. The proposed dataset, called , aims to accelerate progress in this specialized application by utilizing crowdsourced resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help machines learn from people’s annotations. Right now, it takes experts a long time to label data for machine learning. But what if we could get lots of people with different levels of expertise to help? This paper shows how this can be done using Amazon Mechanical Turk and creates a big dataset called to help machines learn about faults in underground imaging. |
Keywords
» Artificial intelligence » Machine learning
