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.
This project helps urban planners, environmental researchers, and cartographers automatically identify and classify different types of land cover and land use from satellite imagery. It takes raw Sentinel-2 satellite images as input and outputs a classification of the land, such as 'residential area,' 'forest,' or 'agricultural land.' This allows users to monitor changes in land use over time without manual review.
100 stars. No commits in the last 6 months.
Use this if you need to quickly and accurately classify large areas of land from satellite photos, especially for environmental monitoring, urban planning, or geographic information system (GIS) applications.
Not ideal if you require very fine-grained, pixel-level segmentation or need to classify land cover in images heavily obscured by clouds, ice, or atmospheric effects.
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License
MIT
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Last pushed
Sep 28, 2023
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