Pre-release

This release is a pre-release and may not be stable for production use.

Overview

cars-rasterize aims to convert a point cloud into a digital surface (or terrain) model with colors.

It is a part of the photogrammetry tool cars extracting Digital Surface Models from satellite images.

Installation

cars-rasterize is available on Pypi and can be installed by:

pip install cars-rasterize

Quick start

  1. Download subsampled_nimes.laz*:
wget https://raw.githubusercontent.com/CNES/cars-rasterize/master/data/subsampled_nimes.laz
subsampled_nimes.laz
drawing

subsampled_nimes.laz* is from https://geoservices.ign.fr/lidarhd. and has been downsampled (1 point every 50cm) to make the file smaller.

  1. Run las2tif executable:
las2tif subsampled_nimes.laz dsm.tif --clr_out clr.tif
  1. ✅ Done! The executable generates two files:
  • dsm.tif: the elevation of the points (Z dimension) are projected into a regular grid to generate a raster file named Digital Surface Model.
  • clr.tif: the red, the green and the blue dimensions can be also projected producing a color interpretation map superimposable on DSM
dsm.tif clr.tif
drawing drawing

How it works

A LAS file contains a set of points $P = {(x, y, z, r, g, b)_k}$ each having several dimensions:

  • $x$ and $y$ correspond to planimetric information
  • $z$ corresponds to the altitude
  • $r$, $g$ and $b$ correspond to colorimetric information (respectively red, green, blue )

To create a raster digital surface model, we define a regular grid on a region of interest roi of origin $(x_{start}, y_{start})$, size $(x_{size}, y_{size})$ with a constant resolution.

For each cell of center $(c_x, c_y)$, we consider the subset of points contained in the disk $D$ (parameter radius) centered on this cell (see figure below):

Contributing points
drawing

Then, the altitude assigned $z(c_x, c_y)$ to the cell is a Gaussian weighted average (standard deviation sigma $\sigma$) of the distance $d$ to its center :

$$z(c_x, c_y) = \frac{\sum_{p_k \in D} z_k e^{-d_k^2/2\sigma^2}}{\sum_{p_k \in D} e^{-d_k^2/2\sigma^2}}$$

Finally, to have a superimposable color to this dsm, the colors are averaged in the same way.

Contribution

cars-rasterize is a free software: Apache Software License 2.0. See Contribution manual.

Project links

Data verified by PyPI on Aug 18, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Stable release available (0.2.3)

3 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for belletva from gravatar.com belletva Avatar for cars-cnes from gravatar.com cars-cnes Avatar for dyoussef from gravatar.com dyoussef

Credits

Author: CNES

GitHub Statistics

Data verified by PyPI on Aug 18, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License (Apache Software License 2.0)

Requires

Python >=3.8

Provides Extra

dev

Tags

cars-rasterize 3D DEM cars pandora photogrammetry

Classifiers

Report project as malware

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0.3.0rc0 This release

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