These are the projects that we trained our classifier because a lot of the current users are processing similar projects.You may notice that your tall stockpiles were recognized as buildings and your sharp-roofed buildings were categorized as high vegetations. Currently, the classifier works the best on projects with flat-roofed buildings, scattered high vegetation, ground-level asphalt roads and with a GSD (ground sampling distance) of around 5 centimeters. so the program can start learning from instruction and experiences. We need to teach it which are trees and which are buildings, etc. Imagine the initial classifier as a newborn baby learning to understand it’s surroundings in a similar way that a human being learns. With the extraction of geometric and color features, we are able to provide a fast classification routine to all Pix4D users.” – Nicolai Häni Teach the algorithm to learn “I am glad to be part of the team that developed the feature using machine-learning technology. Our next steps are to use more training data and post-process the results to provide cleaner and more reliable results.” – Carlos Becker “We are very happy to see the huge response and early adoption of the point cloud classification from our users.
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