Summary of Carbonnet: How Computer Vision Plays a Role in Climate Change? Application: Learning Geomechanics From Subsurface Geometry Of Ccs to Mitigate Global Warming, by Wei Chen et al.
CarbonNet: How Computer Vision Plays a Role in Climate Change? Application: Learning Geomechanics from Subsurface Geometry of CCS to Mitigate Global Warming
by Wei Chen, Yunan Li, Yuan Tian
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces a novel approach using computer vision to predict land surface displacement from subsurface geometry images for Carbon Capture and Sequestration (CCS). By directly training models on these images, the authors aim to overcome limitations of pre-trained models and high computational costs. The goal is to understand the response of land surface displacement due to carbon injection, informing decision-making in CCS projects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses computer vision to predict how the ground moves when storing carbon underground. This is important because we need to make sure this process doesn’t cause problems like earthquakes or damage to buildings. Right now, scientists are struggling with big computers and pre-trained models that don’t work well for complex tasks. The authors came up with a new idea to train their own models directly on the images of what’s happening underground. This can help them better understand how storing carbon affects the ground and make decisions about future projects. |
