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stand_mapping's Introduction

Making a Stand

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.

Project Organization

├── 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

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