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cusicand / lidarhd_ign_downloader

Python script to download and resample LiDAR HD from IGN.
Apache License 2.0
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IGN LiDAR HD downloader

DOI

author: Diego Cusicanqui contributors: Jean Baptiste Barré

contact: diego.cusicanqui@univ-grenoble-alpes.fr

Python scripts to quick download and resample LiDAR HD classified point clouds from IGN-France. This python script can be executed on command line interface (CLI) as well as within a Jupyter Notebook. Look at the How to use section for more details.

Refer to IGN-France website for news about data processing.

Required packages

Clone github repository

In order to install properly, you have to clone lidar_ign_downloader repository.

git clone https://github.com/cusicand/lidarhd_ign_downloader.git
cd ./lidarhd_ign_downloader

Installation

Most of the libraries required for this script are standard and are often pre-installed in conda python environments. Please follow the instructions below depending on your requirements.

If you already have a conda python environment pre-installed, please follow the instruction in section Install on pre-existing conda python environment section. Otherwise, you will need to install a conda python environment to use this script. Instructions are given in the section Install packages on a new conda python environment.

Install packages on an existing python environment

Run the next command lines in your command-line prompt:

conda activate <your-env-name>

or 

conda install -c conda-forge pdal==2.5.6 draco=1.5.6 python-pdal gdal untwine geopandas python-wget pathlib2 tqdm ipykernel pyogrio

We encourage the use of mamba since this library as is faster than conda. If you want to use mamba, run the following lines:

conda activate <your-env-name>
mamba install -c conda-forge pdal==2.5.6 draco=1.5.6 python-pdal gdal untwine geopandas python-wget pathlib2 tqdm

Install packages on a new python environment

If you want to create a specific python environment, please follow the instructions below.

Python environment with Miniconda

Go to the Miniconda website and download the lastest version of Miniconda. Detailed instructions on how to install conda python environments for your operating system are available on the Anaconda website.

Once conda installed, you can

Create the new environment using mamba

First, install mamba

conda install -n base -c conda-forge mamba

then, install all packages using the pdal_env.yml file provided.

mamba env create -f pdal_env.yml

To active the environment, type conda activate pdal_env

Create the new environment using conda

conda env create -f pdal_env.yml

To active the environment, type:

conda activate pdal_env

Make python script executables

If you want to run the script anywhere in your computer from CLI, you need to add the following lines to your .bashrc file to have full access to scripts. Open your .bashrc file using vi ~/.bashrc or nano ~/.bashrc and copy the following lines at the end.

export LIDAR_PATH=$HOME/lidarhd_ign_downloader
export PATH=$LIDAR_PATH:$PATH            
export PYTHONPATH=$LIDAR_PATH:$PYTHONPATH

[!NOTE]
If your installation directory is different than $HOME, Replace $HOME by the full directory path.

Once changes saved, run chmod +x lidar_downloader.py into github repository to ensure the execution of the script.

Use source ~/.bashrc to reload changes.

PDAL_WRENCH installation (optional)

After version 2.0 of lidar_downloader we introduce the possibility to generate point density map to quantitatively estimate the number of points at a given resolution. This tasks is based on pdal_wrench and requires individual installation. A detailed description could be found wrench GitHub. Further investigation are ongoing to better integrate pdal_wrench within lidar_downloader.

Make pdal_wrench scripts executables

Once pdal_wrench installed, it is better you can run it from everywhere in the computer. To do so, add the following lines to your .bashrc.

export PDWRENCH=$HOME/wrench/build/
export PATH=$PDWRENCH:$PATH

[!NOTE] If your installation directory is different than $HOME, Replace $HOME by the full directory path.

HOW TO USE

Command line interface (CLI)

Inside the command line prompt, type python lidar_downloader.py -h to access to the help of the tool.

This small tool needs and Area of Interest (AOI) in .shp or .gpkg format as mandatory argument. Then, we can switch between several parameters like:

Then, we can switch between several parameters like:

Below is an example using the supplied shapefile:

lidarhd_downloader.py aoi_example.gpkg -out_data /home/user/some/path/directory/ -tr 1 -compute_elev mean -dtype gtif

or 

lidarhd_downloader.py aoi_double.shp --out_data_path /home/user/some/path/directory/ --dem_resolution 1 --compute_elevation mean --file_data_type gtif

Jupyter notebook interface

Inside a Jupyter-Notebook, you can also run the lidar_downloader. However, the set up of parameters are is slightly different. You can see and use the example provided within the project.

import lidar_downloader
args_list = ['/path/to/aoi/aoi_example.gpkg',
             '--out_data_path', '/some/path/directory/lidar_ign_test/',
             '--dem_resolution', '1.0',
             '--compute_elevation', 'mean',
             '--file_data_type', 'gtif',
             '--remove_tiles']
parser = lidar_downloader.getparser()
args = parser.parse_args(args_list)
lidar_downloader.main(args)

[!NOTE]
Whatever the case, the script will iterate through all the features (polygons) within the shapefile or geopackage file. It will create a folder for each specific AOI based on the column with the aoi_name. If you used your own shapefile, make sure to have one column called aoi_name. Otherwise, you can edit the provided file.

Contact and citation

For any question/bug/issue, please report it on issues section or contact diego.cusicanqui@univ-grenoble-alpes.fr

[!IMPORTANT]
Please don't be lazy and cite this tool using the following DOI. DOI