Street Segmentation from QGis rasters

How to interact

The images from the diferent sensores should be in a forlder named R10m. The images can be obtained here/$value) and the files are located at GRANULE/L2A_T29TNF_A005497_20180326T112919/IMG_DATA/R10m;
Simply run Street_seg.py and two windows will be displayed.
The left one shows the original satellite raster preview
The right one displays the results obtained after segmentation.

Some visual results are presented at Resultados folder.
Only the TCI raster is used since it is an RGB image built from
the B02 (Blue), B03 (Green), and B04 (Red) Bands.
Therefore there’s no need of reading the images of the 3 individual
sensors.
Because the input is based on a truly HDR image, every time the zoom
(scale) of the windows are changed, the displayed portion of the raster
is normalized for an RGB scale between [0,255]. This is done by taking
into account the maximum value of the pixels of the displayed portion,
allowing a dynamic adjustment based on the light intensity of that portion.
For higher values of zoom, the original raster presents already
some blur. In these scenarios, applying a gaussian blur filter to reduced soft
edges before canny is irrelevant and not useful to the final result.
- An adaptive cauny method is used to detect the edges of the raster.
  The lower and upper limits are estimated based on the median of the image as seen here.
- After applying the canny filters, the green (chlorophyll) zones are
  “cleaned” from the result. This is made base on an analysis of the pixel
  colour in the HSV space and allows to remove edges encounter in places like
  rivers surrounded by green zones or edges between field crops.