vt-land-cover-classification
Science Score: 54.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
✓Committers with academic emails
1 of 2 committers (50.0%) from academic institutions -
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (12.5%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: lasseke
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Size: 10 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
Using machine learning to model the distribution of Vegetation Types in Norway
Project on using supervised classification to predict the distribution of Norwegian
Vegetation Types from environmental background variables.
The code heavily relies on the Google Earth Engine Python API (https://developers.google.com/earth-engine/api_docs)
and numerous supporting Python packages (see environment.yml).
Installation suggestion to run the project code locally
Install Anaconda/Miniconda and Git. In a
terminal where the conda and git commands are available (e.g., Anaconda Prompt), run:
cd [path/to/download/target/directory]
git clone https://github.com/lasseke/vt-land-cover-classification.git
cd ./vt-land-cover-classification
conda env create -f environment.yml
conda activate dmvtnor-env
Subsequently, navigate to the "notebooks" directory for the analysis scripts used in Keetz et al. (in prep.). The notebooks may need to be executed sequentially to reproduce results (ascending file numbering).
Project structure overview
| Directory | File(s) | Summary |
|----------|-------------|:------:|
| data/dict/ | colors.json | Defines colors shared across different plots. |
| | predictors.json | Defines metadata (long names, etc.) for the predictor variables. |
| | spectral_indices.json | Defines long names and band calculation formulas for the spectral indices. |
| | vt_classes.json | Defines metadata (long names, ecosystem group, etc.) for the Vegetation Type classes. |
| data/misc/ | vtdata_5f_spatial_cv_indices.pkl | Stores 10-fold leave-location-out cross-validation indices for VT feature matrix entries. |
| data/interim/ | * | Stores interim outputs of processed datasets. Not included. |
| data/processed/ | * | Stores final outputs of processed datasets. Not included. |
| data/raw/ | * | Stores original datasets. Not included. |
| notebooks/ | 00-*.ipynb | Notebook for minor data-preprocessing. |
| | 01-*.ipynb | Notebooks to retrieve and export data in required formats. |
| | 02-*.ipynb | Notebooks to preprocess the data for model fitting (clean, generate feature matrix, create shared spatial cross validation indices, etc.). |
| | 03-*.ipynb | Notebooks to calculate and visualize data statistics (predictor correlation, etc.). |
| | 04-*.ipynb | Notebooks for model experiments. |
| | A-*.ipynb | Notebooks for experiments run on an HPC cluster for better performance. |
| src/ | *.py | Python helper code. |
Owner
- Name: Lasse Keetz
- Login: lasseke
- Kind: user
- Location: Oslo, Norway
- Repositories: 2
- Profile: https://github.com/lasseke
Citation (citation.cff)
cff-version: 1.2.0 message: "If you use this software, please cite it as below." authors: - family-names: "Keetz" given-names: "Lasse T." orcid: "https://orcid.org/0000-0002-8040-4122" title: "AR18x18 Vegetation Type land cover classification" version: 0.1.0 doi: 10.5281/zenodo.10927026 date-released: 2024-04-04 url: "https://github.com/lasseke/vt-land-cover-classification"
GitHub Events
Total
Last Year
Committers
Last synced: about 1 year ago
Top Committers
| Name | Commits | |
|---|---|---|
| Lasse Torben Keetz | l****k@u****o | 4 |
| Lasse Keetz | 5****e | 2 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: about 1 year ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0