{"id":26511252,"url":"https://github.com/rifatsdas/satellite_machine_learning","last_synced_at":"2025-03-21T02:30:21.003Z","repository":{"id":209974151,"uuid":"358882034","full_name":"rifatSDAS/satellite_machine_learning","owner":"rifatSDAS","description":"Unsupervised and supervised learning for satellite image classification","archived":false,"fork":false,"pushed_at":"2022-03-09T11:18:59.000Z","size":44,"stargazers_count":4,"open_issues_count":0,"forks_count":2,"subscribers_count":3,"default_branch":"main","last_synced_at":"2023-11-30T04:34:06.481Z","etag":null,"topics":["classifications","kmeans-clustering","rasterio","satellite-data","scikit-learn","scikit-learn-python","scikitlearn-machine-learning","sentinel-1","sentinel-2","sentinel-3","sentinels","support-vector-classifier","support-vector-machines"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rifatSDAS.png","metadata":{"files":{"readme":"README.md","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,"governance":null}},"created_at":"2021-04-17T13:10:32.000Z","updated_at":"2023-11-30T04:34:08.526Z","dependencies_parsed_at":"2023-11-30T04:44:11.635Z","dependency_job_id":null,"html_url":"https://github.com/rifatSDAS/satellite_machine_learning","commit_stats":null,"previous_names":["rifatsdas/satellite_machine_learning"],"tags_count":0,"template":null,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rifatSDAS%2Fsatellite_machine_learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rifatSDAS%2Fsatellite_machine_learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rifatSDAS%2Fsatellite_machine_learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rifatSDAS%2Fsatellite_machine_learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rifatSDAS","download_url":"https://codeload.github.com/rifatSDAS/satellite_machine_learning/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244725207,"owners_count":20499557,"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":["classifications","kmeans-clustering","rasterio","satellite-data","scikit-learn","scikit-learn-python","scikitlearn-machine-learning","sentinel-1","sentinel-2","sentinel-3","sentinels","support-vector-classifier","support-vector-machines"],"created_at":"2025-03-21T02:30:20.358Z","updated_at":"2025-03-21T02:30:20.961Z","avatar_url":"https://github.com/rifatSDAS.png","language":"Jupyter Notebook","readme":"# satellite_machine_learning\n\n### Machine learning algorithms with satellite data (optical, radar)\n\nThis repo contains real-life examples of machine learning applications (unsupervised and supervised learning) using satellite raster data.\n\nThe examples are based on unsupervised and supervised classification to investigate different landcovers.\n\nThe classifications are tested with Landsat 4 TM, Landsat 8 OLI, Landsat 7 ETM+, Sentinel 2 MSI, and Sentinel 3 OLCI optical satellite data. All data used for testing is publically available under open source license. For more details look here https://scihub.copernicus.eu/\n\nAny of these classfications in this repo can be applied with any optical satellite data, from space-borne or air-borne sensors.\n\nTo run any of these jupyter file, user needs:\n\nAnacond installed with Python 3.6 or above\nRasterio python package for working with satellite raster data \nTo install rasterio see here: https://rasterio.readthedocs.io/en/latest/\n\nAll machine learning applications are based on SciKit-Learn API https://scikit-learn.org/stable/modules/classes.html\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frifatsdas%2Fsatellite_machine_learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frifatsdas%2Fsatellite_machine_learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frifatsdas%2Fsatellite_machine_learning/lists"}