Non-geospatial images
======================

..
    TODO: Add a note here that says that you should look through step-by-step guidance before engaging with the worked examples to understand the workflow.

While MapReader was developed for maps and geospatial images, the package can also be used for non-geospatial images.
We have provided two examples of this.

Classification of plant phenotypes
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.. image:: /_static/tutorial_classification_plant_phenotype.png
   :width: 400px
   :target: https://github.com/maps-as-data/mapreader-examples/tree/main/notebooks/non-geospatial/classification_plant_phenotype

In our ``classification_plant_phenotype`` example, we demonstrate how to use MapReader to classify plant phenotypes in images of plants.
Importantly, this worked example demonstrates how to use MapReader with non-georeferenced images (e.g. non-georeferenced map images).
It can be found `here <https://github.com/maps-as-data/mapreader-examples/blob/main/notebooks/non-geospatial/classification_plant_phenotype/Pipeline.ipynb>`__.

Classification of MNIST digits
------------------------------

.. image:: /_static/tutorial_classification_mnist.png
   :width: 400px
   :target: https://github.com/maps-as-data/mapreader-examples/tree/main/notebooks/non-geospatial/classification_mnist

In our ``classification_mnist`` example, we demonstrate how to use MapReader to classify MNIST digits.
Importantly, this example demonstrates how to use MapReader to classify whole images instead of patches and therefore how MapReader can generalize to much broader use cases.
It can be found `here <https://github.com/maps-as-data/mapreader-examples/blob/main/notebooks/non-geospatial/classification_mnist/Pipeline.ipynb>`__.