Summary of Land Cover Image Classification, by Antonio Rangel et al.
Land Cover Image Classification
by Antonio Rangel, Juan Terven, Diana M. Cordova-Esparza, E.A. Chavez-Urbiola
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 explores the application of deep learning models for land cover (LC) image classification, which is crucial for understanding environmental changes, urban planning, and disaster management. The authors compare convolutional neural networks (CNNs) with transformer-based methods to improve accuracy and efficiency in LC analysis. The study uses EuroSAT, a patch-based LC classification dataset based on Sentinel-2 satellite images, achieving state-of-the-art results using current transformer models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Land cover image classification is important for understanding environmental changes, urban planning, and disaster management. This paper shows how deep learning can help make this process more accurate and efficient. The authors compare different types of artificial intelligence models to see which ones work best for land cover analysis. They use a special dataset called EuroSAT that helps them test their models. |
Keywords
* Artificial intelligence * Classification * Deep learning * Image classification * Transformer
