Summary of Seafloorai: a Large-scale Vision-language Dataset For Seafloor Geological Survey, by Kien X. Nguyen and Fengchun Qiao and Arthur Trembanis and Xi Peng
SeafloorAI: A Large-scale Vision-Language Dataset for Seafloor Geological Survey
by Kien X. Nguyen, Fengchun Qiao, Arthur Trembanis, Xi Peng
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 A novel AI-ready dataset, SeafloorAI, is introduced for seafloor mapping across 5 geological layers, bridging a gap in sonar imagery analysis. The dataset, curated with marine scientists, features 62 geo-distributed surveys, 696K sonar images, and detailed language descriptions. By incorporating the language component, the extended dataset SeafloorGenAI enables vision- and language-capable models for sonar imagery. The paper aims to engage the marine science community to enrich the data pool and inspire machine learning model development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Marine scientists have been struggling with limited AI-ready datasets for sonar imagery analysis. A new project, SeafloorAI, is trying to fix this problem by creating a huge dataset of seafloor maps. This dataset is special because it includes lots of language descriptions and question-answer pairs, making it possible to train machine learning models that can understand both pictures and words. The goal is to help the marine science community make better use of the data and inspire new AI models. |
Keywords
* Artificial intelligence * Machine learning
