Classes and methods to help with the creation of geospatial training datasets and deep-learning models.
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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:
- 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:
- Segmentation
- subclass of the tensorflow.keras.Model class to be used for image segmentation
- 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.
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