Hello Martin,
I'm currently working on a rail alignment project and require accurate cross sections for the design of slope stability solutions along the rail corridor.
As operations are in a remote and hostile terrain we experimented by deploying a UAV equipped with 20 Megapixel 1 inch CMOS sensor to generate an output with 40 million data points for an area of roughly 1.2 sqkm with GSD of 4.5 cm.
We are stuck on the ground classification stage wherein areas of dense vegetation are been left as holes in the data set and hence our contour plots and cross sections are been left open.
We have been trying to classify ground using a trial version for auto classification tools available in the market.
But the results aren't satisfactory. So can you guide me on how the above goal can be achieved in Rapidlasso?
Best,
Tabish Kalsekar
Mumbai, India