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I will visualize the land use classification with the random forest method
Nana
Over deze dienst
Are you looking for accurate land cover classification using remote sensing data? I offer classification services using the Random Forest algorithm in Google Earth Engine (GEE) to help you extract valuable land use/land cover (LULC) information for your research, environmental analysis, or GIS project.
What I need from you:
- Area of interest (AOI) as a shapefile, GeoJSON, or coordinates
- Preferred classification classes (e.g., water, vegetation, built-up, bare land, etc.)
- Any reference or training data (if available)
Maak kennis met Nana
Nana
- Afkomstig uitIndonesië
- Lid sindsmei 2025
Talen
Indonesisch, Engels
i study about GIS and remote sensing. I use GEE, QGIS, Google Colab, and ENVI to process and analyze data. I specialize in processing satellite imagery, land use/land cover classification, NDVI analysis, change detection, and various remote sensing workflows. Whether you're looking for environmental monitoring, spatial analysis, or custom script development, I'm here to help turn data into actionable insights. Let’s collaborate to bring your geospatial project to life!
Veelgestelde vragen
Do I need to provide training data?
If you have training data (e.g., points or shapefiles with labeled classes), feel free to share it. Otherwise, I can generate training data based on visual interpretation and known land cover features in your area.
How accurate will the classification results be?
I provide accuracy assessment using a confusion matrix and kappa coefficient. Accuracy depends on the quality of training data and the heterogeneity of your area of interest.
In what format will I receive the final output?
You will receive the classification result in GeoTIFF or Shapefile format. I can also provide a visual map layout (PDF or PNG) upon request.
