Stands for finnish word "metsä" which means "forest". So, pymetsa is a library for forest-related spatial data processing

Before start

To use this library there is a need to place the following files in the data folder in this repository:

Documentation

The architecture og the module is multi-layer:

Download - access to data sources using bindings above APIs
Preprocessing - set of functions to preprocess raw spatial data (both raster and vector)
Sample - layer for preparing the data for neural networks training (clipping, augmentation, saving objects into files, etc.)
Model - module for machine learning model fitting

And visualization for creating plots and save them into files.

The examples folder contain all necessary launch demo scenarios for this library

Landsat: https://earthexplorer.usgs.gov/. Dataset: Landsat 8-9 OLI/TIRS C2 L2. Product: Landsat Collection 2 Level-2 Product Bundle

Current module deployed on Heroku using the following instructions:

heroku login

Launch docker daemon and then

heroku container:login

heroku container:push web --app pymetsa-demo

heroku container:release web --app pymetsa-demo

Swagger UI available via URL: In progress

For local launch there is a need to start launch.py

In progress