Manual classification on point clouds
This is why you should do it
Maybe let’s start with this to get one misunderstanding out of the way: manual classification means nothing else than labeling or annotation. Labeling is most often used among the AI community and seen by GIS specialists as manual classification. Annotation would be the more general term, but they all describe the same process:
Assign every point within the point cloud to a respective object class.
Currently there is a lot of inefficient work with point clouds in order to extract information. Pointly offers a way to change that into efficient work. But let’s have a look at two examples to point out the dilemma first:
So, what’s a way to work more efficiently?
You need to manually classify within the point clouds again or start doing it. Even if that feels like taking a step back, it is actually two steps forward. The idea is that you do it once and then rarely ever again. You might ask yourself now “Is this not still a lot of work”? Don’t worry with the help of Pointlys intelligent selection tools the whole classification process will be much easier. Moreover, you can upload, manage and view huge point clouds in our platform.
If you do manually classify, it will greatly benefit you in the future as you can generate training data and faster close the gap between human labeling and machine learning.
You will get better results, can do automated analyses, isolate objects and complete various other tasks, for example automatically generate lines for CAD models afterwards. Thus, you can train a neural network, which automates and accelerates processes like identifying road signs for the n-th time.
The ultimate goal of manual classification is to gather a decent amount of training data in good quality to train the AI. More information concerning the role of AI in combination with point clouds you can find here.
Pointly – Point out what matters.