Created as part of the AI4ER Group Team Challenge 2021 by the Biodiversity Team.

Table of contents:

1. Features
2. Getting started
3. Requirements
4. Project structure

1. Features

GeoGraph provides a full-stack tool for analysing habitat fragmentation, and other related problems. It includes models to predict land cover classes, a method to extract graph structure from the resulting land cover maps and an extensive range of visualisation and analysis tools.

2. Getting started

Clone this repository using

git clone https://github.com/ai4er-cdt/gtc-biodiversity.git

Enter the directory and install the conda environment using

cd gtc-biodiversity
make env

Done, you're ready to go!

3. Requirements

GeoGraph is written in Python 3.8 and builds on NetworkX, ipyleaflet and many more packages. See the requirements directory for a full list of dependencies.

4. Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make init` or `make lint-requirements`
├── README.md          <- The top-level README for developers using this project.
|
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
|   |                     the creator's initials, and a short `-` delimited description, e.g.
|   |                     `1.0_jqp_initial-data-exploration`.
│   ├── exploratory    <- Notebooks for initial exploration.
│   └── reports        <- Polished notebooks for presentations or intermediate results.
│
├── report             <- Generated analysis as HTML, PDF, LaTeX, etc.
│   ├── figures        <- Generated graphics and figures to be used in reporting
│   └── sections       <- LaTeX sections. The report folder can be linked to your overleaf
|                         report with github submodules.
│
├── requirements       <- Directory containing the requirement files.
│
├── 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_loading   <- Scripts to download or generate data
│   │
│   ├── preprocessing  <- Scripts to turn raw data into clean data and features for modeling
|   |
│   ├── models         <- Scripts to train models and then use trained models to make
│   │                     predictions
│   │
│   └── tests          <- Scripts for unit tests of your functions
│
└── setup.cfg          <- setup configuration file for linting rules

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Project template created by the Cambridge AI4ER Cookiecutter.