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 (3 years ago)

Commits 17 (last one 9 months ago)

Stargazers 69 (0 this week)

Watchers 2 (0 this week)

Forks 28

License mit

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RepositoryStats indexes 534,551 repositories, of these raoofnaushad/Land-Cover-Classification-using-Sentinel-2-Dataset is ranked #348,737 (35th percentile) for total stargazers, and #448,591 for total watchers. Github reports the primary language for this repository as Jupyter Notebook, for repositories using this language it is ranked #8,500/14,929.

raoofnaushad/Land-Cover-Classification-using-Sentinel-2-Dataset is also tagged with popular topics, for these it's ranked: deep-learning (#5,684/7827), machine-learning (#5,352/7371), data-science (#1,492/1973), geospatial (#269/361), satellite-imagery (#88/132)

Other Information

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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: 2024-06-01 @ 05:50pm, id: 310095999 / R_kgDOEnuwfw