Summary of Mapping Of Land Use and Land Cover (lulc) Using Eurosat and Transfer Learning, by Suman Kunwar et al.
Mapping of Land Use and Land Cover (LULC) using EuroSAT and Transfer Learning
by Suman Kunwar, Jannatul Ferdush
First submitted to arxiv on: 6 Nov 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 machine learning approach utilizing remote sensing technologies has demonstrated significant potential in environmental management. The study leverages recent advances in AI, computer vision, and earth observation data to achieve unprecedented accuracy in land use mapping. By employing transfer learning and fine-tuning with RGB bands, the researchers achieved an impressive 99.19% accuracy in land use analysis. This breakthrough can inform conservation and urban planning policies, ultimately contributing to more sustainable management of natural resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses artificial intelligence to help us better manage our environment. By using special technologies that allow us to see what’s happening on the ground from space, scientists were able to accurately map different types of land use with a surprising 99% accuracy! This means we can make more informed decisions about where to build cities and how to protect natural areas. |
Keywords
* Artificial intelligence * Fine tuning * Machine learning * Transfer learning
