v1.1.0

This release includes some major updates, including:

• Reduced data throughput requirements by using preprocessed cloud probabilities
• Better removal of clouds and cloud shadows
• Improved mosaicing and tiling of prediction windows
• Improved unit test coverage
• Type hints and docstrings for the majority of code
• Swap float32 file structure to uint16 for reduced storage demand
• Swap numpy array saving to hickle to reduce storage demand
• Improve multitemporal alignment by doing a median correction between interpolated and non-interpolated regions
• Adds example data to the large-area notebooks
• Includes beta integration capabilities with the SEPAL platform

v1.0.0

Source code for "A global method to identify trees inside and outside of forests with medium-resolution satellite imagery"