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English
$
USD
I will build geospatial ai and machine learning models for remote sensing data
About this Gig
Welcome! I specialize in Geospatial Data Science, Machine Learning, and Remote Sensing data processing. I help researchers, businesses, and organizations extract actionable spatial insights from satellite imagery and complex geographic datasets.
What I Offer:
- Satellite Data Extraction & Processing (Sentinel, Landsat, MODIS, Planet)
- Land Use & Land Cover (LULC) Classification & Change Detection
- Machine Learning & Deep Learning Models (XGBoost, Random Forest, PyTorch, TensorFlow)
- Google Earth Engine (GEE) & Spatial Python Pipelines (Rasterio, Shapely, GeoPandas)
- Spatial Cross-Validation & Model Explainability (SHAP/LIME)
- Environmental & Disaster Risk Mapping
Why Work With Me?
- Strong academic and professional background in Computer Science & Remote Sensing
- Clean, modular, and fully documented Python/GEE code
- End-to-end support from raw data preprocessing to interactive visualization
Please contact me before placing an order to discuss your project requirements and data availability!
Programming language:
Python
•
R
•
Colab
Frameworks:
Scikit-learn
•
Keras
•
PyTorch
APIs:
Other
Tools:
Jupyter Notebook
•
OpenCV
•
TensorFlow
•
Colab
•
RStudio
My Portfolio
FAQ
What data formats do I need to provide?
You can provide satellite imagery (GeoTIFF, NetCDF), vector shapes (Shapefile, GeoJSON, KML), or simply point coordinates/region of interest (ROI). I can also fetch public satellite data directly via Google Earth Engine or APIs.
Will I get the full source code?
Yes, all packages include the complete, clean, and commented Python code (Jupyter/Colab notebooks or scripts) along with final spatial outputs.
Can you build custom Machine Learning models for non-standard GIS tasks?
Absolutely! I can design tailored pipelines for custom classification, regression, or anomaly detection based on your specific requirements.
