our entry for the cool farm alliance challenge
create environment with conda :
conda create --name myenv --file environment.yml
conda activate myenv
Create postgres database
make create_postgress
Run Backend
make init
- Retrieve satellite images
Multispectral images are retrieved using Google earth engine thorugh a python script. The following functions allow to download relevant data given a geojson with coordinates of the area of interest:
- Downloading data data.download_dataset(aoi)
- Preprocess images data.make_dataset(dataset name)
-
Detect land cover and segment
models.predict_model()
-
Calculate co2 metrics
-
User interaction (frontend & backend)
download images from google earth engine as zip file aoi : area of interest as json file date_range : list of [start date, end_date] in 'YYYY-MM-DD' format mode : sentinel_raw is the only implemented for now band_names : list of band to keep from the original image defaults to ["B2", "B3", "B4", "B8"] Returns None. saves zip file with image in data/raw folder import data
image = data.get_gee_data()
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── 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`.
│
├── 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
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── 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
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
Project based on the cookiecutter data science project template. #cookiecutterdatascience