Metadata-Version: 2.1
Name: geode-ml
Version: 2.0.0
Summary: Classes and methods to help with the creation of geospatial training datasets and deep-learning models.
Author-email: Matt Reichenbach <matthew.reichenbach@gmail.com>
License: MIT License
        
        Copyright (c) 2022 mpreichenbach
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
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        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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Project-URL: Homepage, https://github.com/mpreichenbach/geode-ml
Keywords: deep-learning,training,dataset,geospatial
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

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.metrics module
--------------------

The metrics module contains useful metrics for testing model performance.

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.
