Predicting Pacific Northwest forest types from remotely-sensed data.
This repository includes data cleaning, model-fitting, and applications of predictive models to estimate basic forest attributes using lidar data, satellite and aerial imagery, and down-scaled climate information.
This effort has been supported by two Conservation Innovation Grants from the USDA Oregon Natural Resources Conservation Service:
- "Technology Transfer for Rapid Family Forest Assessment and Stewardship Planning" - FY 2017 Oregon Conservation Innovation Grant, Award # 69-0436-17-036.
- "Modern Land Mapping Toolkit to Streamline Forest Stewardship Planning" -
FY 2019 Oregon Conservation Innovation Grant, Award # NR190436XXXXG012
This effort has also been supported by a grant of cloud storage and computing services made available to Ecotrust under the Microsoft AI for Earth Program in a project entitled "Mining Public Datasets to Automate Forest Stand Delineation and Labeling."
├── LICENSE
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Working versions of data during processing.
│ ├── processed <- Processed datasets ready for modeling.
│ └── raw <- Raw data
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Reports (PDF, etc.)
│ └── figures <- Generated graphics and figures used in reports.
│
├── environment.yml <- The requirements file for reproducing the analysis environment, e.g.
│ using `conda create env --file environment.yml`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
└── src <- Source code for use in this project.
├── __init__.py <- Makes src a Python module
│
├── data <- Scripts to download or generate data
│
├── features <- Scripts to turn raw data into features for modeling
│
├── models <- Scripts to train models and then use trained models to make
│ predictions
│
└── visualization <- Scripts to create exploratory and results oriented visualization
Project organization based on the cookiecutter data science project template. #cookiecutterdatascience
