Surv-Lukmon/LC-Classification-ML
Assessed the performance of Random Forest (RF) and Support Vector Machines (SVM) machine learning algorithms for land cover classification in a predominant agricultural landscape using the fusion of time-series Sentinel-1 and Sentinel-2.
This project helps environmental managers, urban planners, and agricultural specialists quickly identify different types of land cover, such as water, soil, and vegetation. It takes satellite imagery from Sentinel-1 and Sentinel-2 as input and produces detailed land cover maps. This is particularly useful for tracking changes in agricultural areas and managing natural resources efficiently.
No commits in the last 6 months.
Use this if you need to generate accurate, up-to-date land cover maps for large agricultural landscapes using satellite data.
Not ideal if you require very high-resolution land cover mapping (e.g., finer than 10 meters) or need to classify land use rather than just land cover.
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Last pushed
Jul 07, 2024
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