I will analyze satellite imagery using google earth engine and python

I can perform a wide range of geospatial analyses including land use/land cover mapping, NDVI time-series analysis, rainfall and temperature trends, spatial correlation, and machine learning classification using Google Earth Engine and Jupyter Notebook.

I primarily use Python with libraries such as geopandas, rasterio, xarray, matplotlib, and earthengine-api inside Jupyter Notebook — combined with Google Earth Engine for large-scale geospatial computations.

Yes! I handle both raster (e.g., satellite imagery, climate grids) and vector (e.g., shapefiles, GeoJSON) data efficiently for spatial analysis and visualization.

I use open-source datasets like NASA NEX-GDDP, MODIS, Landsat, Sentinel, and CHIRPS — or you can provide your own custom data.

By default, I provide maps and plots within Jupyter. However, I can also create interactive visualizations using Folium, Plotly, or export results for use in web dashboards (available in premium packages).

You’ll receive outputs in formats such as .ipynb (notebook), .csv, .tif, .shp, .geojson, or .png/.pdf maps depending on your request.