geode-ml 2.7.2

Classes and methods to help with the creation of geospatial training datasets and deep-learning models.

How to install geode-ml

The geode-ml package depends on GDAL and Tensorflow for most of its functionality. It is easiest to install GDAL using the conda package manager:

conda create -n "geode_env" python>=3.7
conda activate geode_env
conda install gdal

However, installing Tensorflow with Conda is trickier; we recommend following official documentation for installing the cuDNN and CUDA Toolkit libraries with the conda package manager (if you have a compatible GPU), and then doing

pip install tensorflow-gpu

After activating an environment which has both GDAL and Tensorflow, use pip to install geode-ml:

pip install geode-ml

The geode.datasets module

The datasets module currently contains the class:

1. SemanticSegmentation
   • creates and processes pairs of imagery and label rasters for scenes

The geode.losses module

The losses module contains custom loss functions for model training; these may be removed in the future when implemented in Tensorflow.

The geode.models module

The models module contains the classes:

1. Segmentation
   • subclass of the tensorflow.keras.Model class to be used for image segmentation
2. Unet
   • subclass of the Segmentation class which instantiates a Unet architecture.

The geode.utilities module

The utilities module currently contains functions to process, single examples of geospatial data. The datasets module imports these functions to apply to batches of data; however, this module exists so that methods can be used by themselves, without instantiating a class object from another module.