Feature | Variable_name | Definition | Ranges/Categories Adopted |
---|---|---|---|
contributor | dataset | Contrinutor ID | |
DOI | NA | Paper ID | |
First Author and publication year | AuthorYear | Name(s) of authors | First auther and publication year used as study label. |
Location | location | Location of the data used (country level) | |
Overall Accuracy* | OA_reported | Effect size of interest | |
Sample Size* | sample_size | The sample size (i.e.: number of pixels, or objects) | |
Publication Year | Publication_Year | Year of publication | |
Classification Type | classification_type | Unit of analysis in the primary study | Object-level, Pixel-level, Unclear |
Model Group* | model_group | Type of algorithm used. Any group that makes up less than 5 is regrouped as other analysis | Use abbreviations: Decision Tree (DT), Discriminant Analysis (DA), Fuzzy (FZ), Genetic Algorithm (GA), Immune System (IS), Index-Based (IB), K-Nearest Neighbor (KNN), Maximum Likelihood (ML), MinimumDistance (MD), Neural Network (NN), Parallelepiped (PP), Random Forest (RF), Spectral Angle Mapper (SAM), Subspace (SS), and Support Vector Machines (SVM), Ot |
Ancillary Data | ancillary | Use of non-RS data in the model | Remote Sensing Only, Ancillary Data Included |
Indices | indices | Use of indices to enhance analysis | Used, Not Used |
Remote Sensing Type | RS_device_type | Category of remote sensing | Active, Passive, Combined, Not Reported |
Device Group | RS_device_group | Specific device extracted, then grouped | Landsat, Sentinel, Other, Not Reported |
Number of Spectral Bands | RS_spectral_bands_no | Number of spectral bands used | Count the number of bands or NA |
Spectral Bands group* | no_band_group | Number of spectral bands is regrouped | Low:1-4 , Mid:5-20,high >20, Not Reported:NA |
Spatial Resolution | RS_spatital_resolution_m | Spatial resolution in meters | eg: 30, <1, NA |
Confusion Matrix* | Confusion_matrix | Whether a confusion matrix was present | Reported, Not Reported |
Majority-class Proportion* | fraction_majority_class | The proportion of the largest class | |
Device | Rs_devices | Type of remote sensing device | Satellite, Aerial Photographic Images |
Instructions
How to add to the dataset
Sadly the current set-up is a bit convoluted and geared towards GitHub users. For a introduction on using Git and GitHub through R see: Happy Git and GitHub for the useR.
If you have an GitHub account:
Fork the repository
Go to the GitHub repository and click the “Fork” button. This creates a copy of the repository in your GitHub account.
Clone Your Fork
In your forked repository, click the green “Code” button, copy the URL, and clone it to your local machine using Git:
git clone https://github.com/your-username/your-forked-repo.git
Update the CSV File
In your cloned repository, navigate to the CSV file (e.g., data/Contrib_metaData.csv).
Open it and add your data in the same format as the existing rows.
Save the file.
Commit and Push Your Changes
git add data/quotes.csv git commit -m "Added new data to the CSV" git push origin main
Submit a Pull Request
Go back to your fork on GitHub and click “Contribute” > “Open Pull Request.”
Submit your pull request, and I will review and merge your changes!
Codebook
All variables that are in the dataset with the categories and explaintions