/Patch_wise_Land_Cover_Classification

Patch-wise Land Cover Classification using Sentinel-2 Satellite and Deep Learning

Primary LanguageJupyter NotebookMIT LicenseMIT

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Patch-wise Land Cover Classification using Sentinel-2 Satellite and Deep Learning

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Growing population, urban sprawl, changes in land cover, and land use result in an increased need for systematic and accurate land cover and land use information. Highly accurate information on land cover and land use is essential for decision-makers, urban planners, mapping of ecosystem services, deforestation analysis, detection of land cover changes, and many others. Satellite imagery is recognized as one of the most important data sources for land cover mapping, monitoring the dynamics of the land cover changes at local, regional, national, and global scales. The statistical metrics based on test data show 75% overall accuracy, 75%, and 74% weighted average for recall and precision, respectively.