Summary of Samodified: a Foundation Model-based Zero-shot Approach For Refining Noisy Land-use Land-cover Maps, by Sparsh Pekhale et al.
SAModified: A Foundation Model-Based Zero-Shot Approach for Refining Noisy Land-Use Land-Cover Maps
by Sparsh Pekhale, Rakshith Sathish, Sathisha Basavaraju, Divya Sharma
First submitted to arxiv on: 17 Dec 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 tackles the problem of automating land-use and land cover (LULC) map generation using machine learning, a crucial task in remote sensing with applications across agriculture, utilities, and urban planning. The challenge lies in noisy labels from ground truths like ESRI LULC and MapBioMass, which can distort performance metrics and hinder accurate pixel classification. To overcome this, the authors propose a zero-shot approach using the Segment Anything Model (SAM) to delineate land parcels and relabel unsure pixels based on local label statistics. This approach achieves a significant reduction in label noise and improves downstream segmentation model performance by approximately 5%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists are trying to make it easier to create maps that show what’s happening on the Earth’s surface. Right now, this is a difficult task because the labels used to teach machines to do this job are often wrong. This makes it hard for machines to learn and creates misleading results. The researchers came up with a new way to fix these mistakes by using a special kind of model that can help correct the errors. This approach works really well, making it possible to create more accurate maps. |
Keywords
» Artificial intelligence » Classification » Machine learning » Sam » Zero shot
