Repo Dosen ULM

Implementation of Deep learning Based Sematic Segmentation Method to Determine Vegetation Density (Similarity Test)

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dc.contributor.author Sari, Yuslena
dc.contributor.author Arifin, Yudi Firmanul
dc.contributor.author Novitasari, Novitasari
dc.contributor.author Faisal, Mohammad Reza
dc.date.accessioned 2023-04-13T04:03:50Z
dc.date.available 2023-04-13T04:03:50Z
dc.date.issued 2022-09-27
dc.identifier.citation Yuslena Sari, Yudi Firmana Arifin, Novitasari Novitasari, Mohammad Reza Faisal (2022). Implementation of Deep learning Based Sematic Segmentation Method to Determine Vegetation Density (Similarity Test). Eastern-European Journal of Enterprise Technologies, 2022, 5(2-119), pp. 42–54 en_US
dc.identifier.issn 1729-3774
dc.identifier.uri https://repo-dosen.ulm.ac.id//handle/123456789/28355
dc.language.iso en en_US
dc.publisher Eastern-European Journal of Enterprise Technologies, 2022, 5(2-119), pp. 42–54 en_US
dc.subject Similarity Test, Deep Learning, Vegetasi Density en_US
dc.title Implementation of Deep learning Based Sematic Segmentation Method to Determine Vegetation Density (Similarity Test) en_US
dc.type Other en_US


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