https://github.com/tibroc/floodfill
This is an implementation of the floodfill algorithm for fire event detection as described in Archibald & Roy 2009. For docs see:
https://github.com/tibroc/floodfill
fire-detection fire-ecology floodfill remote-sensing
Last synced: 3 months ago
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This is an implementation of the floodfill algorithm for fire event detection as described in Archibald & Roy 2009. For docs see:
- Host: GitHub
- URL: https://github.com/tibroc/floodfill
- Owner: tibroc
- License: mit
- Created: 2020-06-05T09:21:47.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-12-26T19:00:39.000Z (over 2 years ago)
- Last Synced: 2025-01-30T01:28:35.519Z (5 months ago)
- Topics: fire-detection, fire-ecology, floodfill, remote-sensing
- Language: Python
- Homepage: https://tibroc.github.io/floodfill/
- Size: 6.01 MB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 5
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
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README
floodfill
=========
.. image:: https://travis-ci.com/tibroc/floodfill.svg?branch=master
:target: https://travis-ci.com/tibroc/floodfill
.. image:: https://coveralls.io/repos/github/tibroc/floodfill/badge.svg?branch=master
:target: https://coveralls.io/github/tibroc/floodfill?branch=master
This is an implementation of the floodfill algorithm for fire event detection as described in:
| Archibald, Sally & Roy, David. (2009).
| Identifying individual fires from satellite-derived burned area data.
| III-160 . 10.1109/IGARSS.2009.5417974.
Installation
------------
To install this python package you need to download the repository,
navigate to the root folder of it and install it using the ``setup.py``:
.. code-block:: bash
python setup.py install
Usage
-----
The program can be executed as a python module:
.. code-block:: bash
python -m floodfill -h
The program provides some command line parameters that let you define its behavior.
The ``-h`` flag will give you an overview over the options and how to use it.
A simple test run can be done like this:
.. code-block:: bash
mkdir output
python -m floodfill --input=tests/test_data.tif --output-folder=output -b
Parallelization
~~~~~~~~~~~~~~~
If you have several files to process (in recursive mode), you can parallelize
processing by setting ``--n-workers`` to a value that suits your number of cores
(if you specify a higher number of workers than you have cores, the program will
automatically take the maximum number of cores available).
.. code-block:: bash
mkdir output
python -m floodfill --input=tests/test_data.tif\
--output-folder=output\
--n-workers=4