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Urban PointCloud Processing

This repository contains methods for the automatic classification and labeling of Urban PointClouds using data fusion. The methods can serve as inspiration, or can be applied as-is under some specific assumptions:

1. Usage in The Netherlands (The "Rijksdriehoek coordinate system");
2. Point clouds in LAS format and tiled following specific rules; and
3. Fusion with AHN and BGT public data sources.

Example notebooks are provided to demonstrate the tools.

Project Goal

The goal of this project is to automatically locate and classify various assets such as trees, street lights, traffic signs, and other street furniture in street level point clouds. A typical approach would be to build and train a machine learning classier, but this requires a rich labeled dataset to train on. One of the main challenges in working with 3D point cloud data is that, in contrast to 2D computer vision, no general-purpose training sets are available. Moreover, the sparsity and non-uniform density of typical point clouds makes transferring results form one task to another difficult.

However, since we are working with urban street level data, we do have access to a large number of public datasets and registries that we can use to start labeling and create an initial training set. This repository contains several data fusion methods that combine public datasets such as elevation data, building footprints, and topographic registries to automatically label point clouds.

We also provide some post-processing methods that further fine-tune the labels. For example, we use region growing to extend the facade of buildings to include protruding elements such as balconies and canopies that are not included in the building footprint.

For a quick dive into this repository take a look at our complete solution notebook.

Folder Structure

• datasets Demo dataset to get started
  • ahn AHN data
  • bgt BGT data
  • pointcloud Example urban point cloud
• media Visuals
  • examples
• notebooks Jupyter notebook tutorials
• scripts Python scripts
• src/upcp Python source code
  • analysis Dataset analysis code
  • fusion Data fusion code
  • preprocessing Pre-processing code
  • region_growing Region growing code
  • scrapers Data scrapers
  • utils Utility functions

Installation

This code has been tested with Python >= 3.8 on Linux and MacOS.

1. To use this code in development mode simply clone the repository and install the dependencies.

   # Clone the repository
   git clone https://github.com/Amsterdam-AI-Team/Urban_PointCloud_Processing.git
   
   # Install dependencies
   cd Urban_PointCloud_Processing
   python -m pip install -r requirements.txt
   

   or, with Conda:

   conda env create -f environment.yml
   

2. Alternatively, the code can be installed as a Python package from source:

   # Install the latest release as Wheel
   python -m pip install https://github.com/Amsterdam-AI-Team/Urban_PointCloud_Processing/releases/download/v0.3/upcp-0.3-py3-none-any.whl
   
   # Alternatively, install the latest version from source
   python -m pip install git+https://github.com/Amsterdam-AI-Team/Urban_PointCloud_Processing.git#egg=upcp
   
   # Or, after making changes in the code
   cd Urban_PointCloud_Processing
   python -m pip install .
   

   If you use the latter and want your code changes to take effect without re-installing the package, use the --editable flag for pip.

3. Additionally, install cccorelib and pycc from the CloudCompare-PythonPlugin project by following the summary instructions below; for more details and Windows instructions see their GitHub page. Please note, these two packages are not available on the Python Package Index (PyPi).

   Building these packages requires Qt.

   git checkout https://github.com/tmontaigu/CloudCompare-PythonPlugin.git
   cd CloudCompare-PythonPlugin
   pip install --upgrade pip  # Requires version >= 21.1
   

   # For Mac OS
   export CMAKE_PREFIX_PATH=/usr/local/opt/qt@5
   

   pip install wrapper/cccorelib
   pip install wrapper/pycc
   

Usage

We provide tutorial notebooks that demonstrate how the tools can be used.

For visualisation of the resulting labelled point clouds we suggest CloudCompare. Simply open the labelled .laz in CloudCompare, select the cloud, and set Colors to the custom Scalar Field named label. For best results, use our custom "AMS" color scale.

Citing our work

If you use (parts of) this repositiory in your work, please cite our paper:

@article{bloembergen2021automatic,
  title={Automatic labelling of urban point clouds using data fusion},
  author={Bloembergen, Daan and Eijgenstein, Chris},
  journal={arXiv preprint arXiv:2108.13757},
  year={2021}
}

Acknowledgements

This repository was created by Amsterdam Intelligence for the City of Amsterdam.

We owe special thanks to Dr. Sander Oude-Elberink for ideas and brainstorming regarding data fusion with AHN and BGT data.