I will classify land cover using satellite imagery and machine learning
About this Gig
I will create land cover classification maps using satellite imagery and geospatial analysis. This service is useful for agriculture, forestry, environmental monitoring, urban planning, research, and land management projects.
I can classify your area into land cover classes such as vegetation, forest, agriculture, water, bare soil, built-up area, rangeland, wetlands, or custom classes based on your project needs. I can work with Sentinel-2, Landsat, GeoTIFFs, existing raster data, training samples, or reference maps.
Depending on the package, I can provide a basic land cover map, class-wise area statistics, GIS-ready output files, accuracy assessment if validation data is available, and a short interpretation report.
Deliverables can include PNG maps, GeoTIFF, shapefile, GeoPackage, CSV statistics, and PDF summary. Please provide your AOI boundary, required classes, target year or season, and any training/reference data if available.
Programming language:
Python
Frameworks:
Scikit-learn
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Keras
•
Panda
Tools:
Jupyter Notebook
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MLflow
•
Colab
