Summary of Deep Learning Meets Obia: Tasks, Challenges, Strategies, and Perspectives, by Lei Ma et al.
Deep Learning Meets OBIA: Tasks, Challenges, Strategies, and Perspectives
by Lei Ma, Ziyun Yan, Mengmeng Li, Tao Liu, Liqin Tan, Xuan Wang, Weiqiang He, Ruikun Wang, Guangjun He, Heng Lu, Thomas Blaschke
First submitted to arxiv on: 2 Aug 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 Deep learning has revolutionized remote sensing applications, particularly at the pixel or patch level. However, its potential in object-based image analysis (OBIA) remains largely untapped. This paper conducts a comprehensive review of OBIA’s task subdomains, with and without deep learning integration. We identify five strategies to address the limitations of deep learning in processing unstructured object data within OBIA and recommend future research directions. Our goal is to inspire exploration in this underexplored area and facilitate the integration of deep learning into OBIA workflows. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how deep learning can be used with object-based image analysis (OBIA). OBIA helps us analyze images by looking at objects rather than individual pixels. Right now, people are using OBIA more often, but they’re not using deep learning as much as they could. This paper looks at why that is and what we can do to make it work better. |
Keywords
* Artificial intelligence * Deep learning
