Applying computer vision approaches to instance segmentation to delineate forest stands and patches using aerial/satellite imagery and lidar data.
Documentation is hosted at Read the Docs.
├── LICENSE
├── README.md <- You're reading it
├── data
│ ├── external <- Data from third party sources
│ ├── raw <- Data ready for processing (e.g., unzipped)
│ ├── interim <- Intermediate data that has been transformed
│ └── processed <- The final, canonical data sets for modeling
│
├── docs <- Documentation using Sphinx and Read the Docs
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ underscore, then a short `-` delimited description, e.g.
│ `01_initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── environment.yml <- Packages needed to reproduce the computing environment using
│ conda package manager
│
├── setup.py <- makes project pip installable (pip install -e .) so stand_mapping can be imported
└── stand_mapping <- Source code for use in this project
├── __init__.py <- Makes stand_mapping 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 visualizations