https://github.com/azeemk210/classification-of-land-cover
Science Score: 23.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
○codemeta.json file
-
○.zenodo.json file
-
✓DOI references
Found 1 DOI reference(s) in README -
✓Academic publication links
Links to: springer.com -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (3.7%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: azeemk210
- Language: Jupyter Notebook
- Default Branch: main
- Size: 1.35 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
"We will classify the Landsat image we've been working with using a supervised classification approach which incorporates the training data we worked with in chapter 4. Specifically, we will be using the RandomForest (Brieman 2001) ensemble decision tree algorithm by Leo Breiman and Adele Cutler. The RandomForest algorithm has recently become extremely popular in the field of remote sensing, and is quite fast when compared to some other machine learning approaches (e.g., SVM can be quite computationally intensive). This isn't to say that it is the best per se; rather it is a great first step into the world of machine learning for classification and regression.\n",
Owner
- Name: azeemk210
- Login: azeemk210
- Kind: user
- Repositories: 1
- Profile: https://github.com/azeemk210