{"id":22193034,"url":"https://github.com/tibroc/floodfill","last_synced_at":"2025-03-24T21:18:12.716Z","repository":{"id":39879147,"uuid":"269584987","full_name":"tibroc/floodfill","owner":"tibroc","description":"This is an implementation of the floodfill algorithm for fire event detection as described in Archibald \u0026 Roy 2009. For docs see:","archived":false,"fork":false,"pushed_at":"2022-12-26T19:00:39.000Z","size":6300,"stargazers_count":2,"open_issues_count":5,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-30T01:28:35.519Z","etag":null,"topics":["fire-detection","fire-ecology","floodfill","remote-sensing"],"latest_commit_sha":null,"homepage":"https://tibroc.github.io/floodfill/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tibroc.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-06-05T09:21:47.000Z","updated_at":"2022-03-16T14:36:49.000Z","dependencies_parsed_at":"2023-01-31T00:45:22.554Z","dependency_job_id":null,"html_url":"https://github.com/tibroc/floodfill","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tibroc%2Ffloodfill","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tibroc%2Ffloodfill/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tibroc%2Ffloodfill/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tibroc%2Ffloodfill/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tibroc","download_url":"https://codeload.github.com/tibroc/floodfill/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245352092,"owners_count":20601093,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["fire-detection","fire-ecology","floodfill","remote-sensing"],"created_at":"2024-12-02T12:46:16.229Z","updated_at":"2025-03-24T21:18:12.693Z","avatar_url":"https://github.com/tibroc.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"floodfill\n=========\n\n.. image:: https://travis-ci.com/tibroc/floodfill.svg?branch=master\n    :target: https://travis-ci.com/tibroc/floodfill\n.. image:: https://coveralls.io/repos/github/tibroc/floodfill/badge.svg?branch=master\n    :target: https://coveralls.io/github/tibroc/floodfill?branch=master\n\n\nThis is an implementation of the floodfill algorithm for fire event detection as described in:\n\n    | Archibald, Sally \u0026 Roy, David. (2009).\n    | Identifying individual fires from satellite-derived burned area data.\n    | III-160 . 10.1109/IGARSS.2009.5417974.\n\n\nInstallation\n------------\n\nTo install this python package you need to download the repository,\nnavigate to the root folder of it and install it using the ``setup.py``:\n\n.. code-block:: bash\n\n    python setup.py install\n\n\nUsage\n-----\n\nThe program can be executed as a python module:\n\n.. code-block:: bash\n\n    python -m floodfill -h\n\nThe program provides some command line parameters that let you define its behavior.\nThe ``-h`` flag will give you an overview over the options and how to use it.\n\nA simple test run can be done like this:\n\n.. code-block:: bash\n\n    mkdir output\n    python -m floodfill --input=tests/test_data.tif --output-folder=output -b\n\n\nParallelization\n~~~~~~~~~~~~~~~\n\nIf you have several files to process (in recursive mode), you can parallelize\nprocessing by setting ``--n-workers`` to a value that suits your number of cores\n(if you specify a higher number of workers than you have cores, the program will\nautomatically take the maximum number of cores available).\n\n.. code-block:: bash\n\n    mkdir output\n    python -m floodfill --input=tests/test_data.tif\\\n        --output-folder=output\\\n        --n-workers=4\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftibroc%2Ffloodfill","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftibroc%2Ffloodfill","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftibroc%2Ffloodfill/lists"}