raoofnaushad / Land-Cover-Classification-using-Sentinel-2-Dataset

Application of deep learning on Satellite Imagery of Sentinel-2 satellite that move around the earth from June, 2015. This image patches can be trained and classified using transfer learning techniques.

Date Created 2020-11-04 (4 years ago)

Commits 17 (last one about a year ago)

Stargazers 81 (0 this week)

Watchers 2 (0 this week)

Forks 31

License mit

Ranking

RepositoryStats indexes 595,856 repositories, of these raoofnaushad/Land-Cover-Classification-using-Sentinel-2-Dataset is ranked #339,331 (43rd percentile) for total stargazers, and #485,301 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #8,468/17,543.

raoofnaushad/Land-Cover-Classification-using-Sentinel-2-Dataset is also tagged with popular topics, for these it's ranked: deep-learning (#5,628/8512), machine-learning (#5,341/8063), data-science (#1,487/2138), geospatial (#262/384), transfer-learning (#191/314), satellite-imagery (#86/145)

Other Information

raoofnaushad/Land-Cover-Classification-using-Sentinel-2-Dataset has Github issues enabled, there is 1 open issue and 0 closed issues.

All Topics

geospatial sentinel-2 data-science deep-learning geospatial-data machine-learning satellite-imagery transfer-learning land-use-classification land-cover-classification

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3 commits on the default branch (master) since jan '22

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The primary language is Jupyter Notebook but there's also others...

updated: 2024-12-17 @ 05:45pm, id: 310095999 / R_kgDOEnuwfw