Dear readers. For me this is a first try at machine learning, so please talk to me like a five year old!
In the Netherlands we have 3d maps of our rivers. I want, with the help of KNIME/ML to automatically determine the type of shore. To make it easy for now lets use 1 type: Nature Friendly Shore (NFS), and ‘type 2’ NOT a NFS.
Type 1 roughly looks like this
The angle is always a little bit different; bet never near vertical.
My current plan of attack looks like this:
1/ Determine vector (Blue arrows) of the river. Exporting the purple line to excel will create 2d x,y table with which I can determine de vector AND de geo location.
2/ Create the section cut; should be 90 degrees angle form the vector (Orange below)
Section cut from number 2; left and right likely to be a NFS.
The 3d model:
3/ Split section cut:
4/ We can create a X,Y plot; lets say N x point resolution per side.
With this we get a list of all section cuts and the x/y coordinates from the 2d section cut:
Section: x1 y1 x2 y2 x3 y3 until N
1Left 0 200 1 180 2 150 etc…
De x,y coordinates will be the input layers of the model.
I have some geo data that dictates, in specific area’s, which parts are NFS’s. So I could use this to create a learning dataset.
Questions if you think this is a good plan of attack:
1/ How do I automate the conversion from XYZ 3D data to section cuts based on the vectors? I think I have the data i need, but how to convert it to XYZ 2D lets say? There should also be a geo element to this; because I need to now the geo location of each and every section cut!
2/ I have no idea of performance. The section cuts I placed above have a per side resolution of around 25 (50 in total) So there will be 25*2 input layers (2 because of x AND y)
I don’t mind if the whole river takes a week to calculate. Months on the contrary would be a little bit too long
I would really appreciate any and all input !
Thanks in advance!!