This repo contains the code lidarForFuel written with R and developped by Olivier Martin : that code be re-written to accommodate specified changes to industralization.
lidarforfuel aims to compute fuel metrics from airborne LiDAR data and map them at a large scale. Currently, two R functions have been developed: 1) fPCpretreatment: pretreatment of a point cloud and 2) fCBDprofile_fuelmetrics: computing fuel metrics. These functions can be used either at the plot scale for specific analyses on small areas or at a large scale using a catalog of LiDAR tiles from the lidR package.
It is important to note that the function fCBDprofile_fuelmetrics for computing fuel metrics/profile needs as entry a pretreated point cloud obtained with the fPCpretreatment.
This library can be used in different ways:
- directly from sources:
make installcreates a mamba environment with the required dependencies - installed with
pipfrom pypi:pip install lidar_for_fuel - used in a docker container: see documentation Dockerfile
.github/: folder containing issue templates and GitHub Actions;.vscode/: folder containing a VS Code configuration for the project;doc/: folder containing documentation .md files (e.g., install.md);img/: folder containing images;tests/: scripts and instructions for running tests;README.md: this file
Every time the code is changed, think of updating the version file: lidar_for_fuel/_version.py
Please log your changes in CHANGELOG.md
To lint the code automatically on commit, install the precommit hooks with make install-precommit
Create the conda environment: make install
Run unit tests: make testing
To generate a pip package and deploy it on pypi, use the Makefile at the root of the repo:
make build: build the librarymake install: update environment with mambamake deploy: deploy it on pypi
To build a docker image with the library installed: make docker-build
To test the docker image: make docker-test
| Nom | Prénom | fonction | |
|---|---|---|---|
| DUPAYS | Malvina | malvina.dupays@ign.fr | DSI IGN |