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While the area of interest for this work is 'Amsterdam' and its nearby regions. But for this sample, AHN3 dataset, provided by the Government of The Netherlands is considered, which is one of the highest quality open datasets available currently, in terms of accurate labels and point density.

Generate 3D building multipatches, from classified building points using 'ArcGIS Pro' and 'City Engine'.Īny airborne point cloud dataset and area of interest can be used.

Classify building points using API's PointCNN model, where we train it for two classes: viz.

Additional sample notebooks related to PointCNN can be found in the sample notebook section on the website.īefore proceeding through this notebook, it is advised that you go through the API reference for PointCNN ( prepare_data(), Transform3d(), and PointCNN()), along with the resources and tool references for point cloud classification using deep learning in ArcGIS Pro, found here.

MULTIPATCH PC HOW TO

This work can also be used for guidance, in other relevant use-cases for various objects of interest.įirst, deep learning capabilities in 'ArcGIS API for Python' are utilized for point cloud classification, then 'ArcGIS Pro' and 'City Engine' are used for the GIS-related post-processing.įurther details on the PointCNN implementation in the API (working principle, architecture, best practices, etc.), can be found in the PointCNN guide, along with instructions on how to set up the Python environment. Within the scope of this sample, we are only interested in 'digital twins of buildings' (3D building multipatches/models).

The workflow described in this sample is about going from raw unclassified point clouds to digital twins: near-perfect representation of real-world entities.

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