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Introduction
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``Landsifier`` is a Python based library to estimate likely triggers of mapped landslides.
The Beta version of library consitute three machine learning based method for finding the trigger of Landslide inventories.

- Geometric feature based method
- Topological feature based method
- Image based method

Sample output of each lmethod
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The below plot shows the probability of each landslide polygons in testing inventory belonging to earthquake and rainfall-induced class. The majority trigger of
landslides is the final trigger of the testing inventory.

.. image:: Images/sample_output.png
   :width: 1200 




1. Geometric feature based method
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This method is based on using 2D landslide polygon geometric properties for classification. This method calculates various geometric properties of landslide polygon and these geometric properties are used as a feature space for machine learning based algorithm. 

Sample landslide polygons
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The below plot shows the sample landslide polygons of earthquake and rainfall-induced inventories.
   
.. image:: Images/sample_landslide_polygons.png
   :width: 1200 

Geometric properties of landslide polygon
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The geometric properties of landslide polygons used are:-

- Area (A) of landslide Polygon
- Perimetre (P) of Landslide Polygon
- Ratio of Area (A) to Perimetre(P)
- Convex hull based measures (Ratio of area of polygon to area of convex hull fitted to polygon)
- Width of minimum area bounding box fitted to polygon
- Eccentricity of ellipse fitted to polygon having area A and perimetre P
- minor-axis of ellipse fitted to polygon having area A and perimetre P

The below plot shows the various geometric properties of landslide polygon

.. image:: Images/polygon_properties.png
   :width: 1200 






2. Topological feature based method
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This method convert 2D landslide polygon to 3D landslide shape by including elevation information.``Landsifier`` library compute topological
features of 3D landslide shape use topological data analysis. These topological features can be used in machine learning algorithm for landslide triggers
classification.

Sample 3D landslide shape
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The below plot shows the sample 3D landslide shape of earthquake and rainfall-induced inventories.

.. image:: Images/3dlandslide_samples.png
   :width: 1200 
   
Coversion of 2D landslide polygons to 3D landslide shape
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The below plot shows the method for coversion of 2d landslide polygon to 3D shape.

.. image:: Images/conversion2d_to3d.png
   :width: 1200 



3. Image based method
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This method convert landslide polygon data to landslide polygon Images. These converted landslide images are used as a input to 
Convolutional Neural Networks for landslide classification.

Sample Polygon Images 
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The below plot shows the sample landslide polygon Images.

.. image:: Images/sample_landslideimages.png
   :width: 1200 
   
Coversion of 2D landslide polygons to Images
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The below plot shows the method for coversion of 2d landslide polygon to grayscale binary images.

.. image:: Images/conversion_polygon_toimage.png
   :width: 1200