classification_raster

All classification will be based upon both supervised and unsupervised machine learning with fuzzy logic, because targets are highly affected by imperfect information due to cultural and natural in a temporal context which might expand several thousand years.

Left: Automatically detected start segments (blue) in front of a shaded relief map, plan view of a motorway junction; Right: Perspective view of the resulting automatically modelled and post-processed break lines (viewing direction: north-west); Data: River Main, German Federal Institute of Hydrology. Mandlburger et al. 2010

Left: Automatically detected start segments (blue) in front of a shaded relief map, plan view of a motorway junction; Right: Perspective view of the resulting automatically modelled and post-processed break lines (viewing direction: north-west); Data: River Main, German Federal Institute of Hydrology. Mandlburger et al. 2010

Automatic extraction of breaklines from digital surfaces:

Label pixels as ‘slope’ or ‘flat’ pixel corresponding to their tilt in xy space.
Breaklines are then detected as borders between slope and flat areas by checking 8-neighbourhood of every flat pixel. If at least one slope pixel is present, the center pixel is classified as breakline pixel.

 

 

 

 

 

 

 

 

 

 

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