Context

To kick off the implementation of classification layers in an object-oriented way, the first step is to define a base ClassificationLayer class. This class will act as the foundation for all specific classification types (e.g., SegmentationClassificationLayer, SlopeClassificationLayer, FusionClassificationLayer). The purpose of the base class is to encapsulate the common functionality shared by all classification types while allowing flexibility for subclasses to implement their specific logic.

Code

In a new python file classification.py in xDEM source code, write the ClassificationLayer abstract class:

1. Define Common Attributes: The base class should define attributes that will be common accross all classification layers, such as:
   • dem DEM on which the classification will be applied to.
   • name name for the classification layer.
   • req_stats list of required statistics to compute (optional, all statistics in geoutils.Raster computed by default).
   • req_stats_classes list of the classes on which the statistics will be applied (optional, all classes by default).
   • class_names dict connecting the class names to the class indexes (set to None).
   • classification result of the classification, a geoutils.Mask object, (set to None).
   • stats_dict required statistics for required classes in a dict (set to None).
2. Abstract Method: Since different classification layers will have their own classification logic (e.g., segmentation masks vs. slope ranges), the base class should declare abstract methods that the subclasses are required to implement, such as apply_classification(). This method will be used to compute the classification attribute, which will be a geoutils.Mask object, in which each band will represent one class mask.
3. Statistics computation: A common feature across all classification layers is the ability to compute statistics on the classified pixels (e.g., mean, standard deviation). The base class should provide a get_stats() method that takes into account the two last attributes. The output, as stats attribute, should be a dict, in which the first layer represent the classes, and the second the statistics. This method should use for each required classes the DEM.set_mask() to apply the classification mask and the DEM.get_stats() method to compute the required statistics. The result will be a dict stored under stats attribute.
4. Saving results: An other common feature is the ability to save the results. The save() method shoud have an output_dir in input, and save:
   • The classification object with the Mask.save() method, under name.tif;
   • The class_name attribute, that represents the name of each class in a dict, under name_classes.json;
   • The stats attribute, under name_stats.json OR name_stats.csv.

Documentation

We need to start a documentation page on this subject.