Programmatic Spatial Computing & ETL
Geospatial Python replaces repetitive desktop software tasks with scalable, testable, and automated processing pipelines. By combining geometric primitives, array operations, and spatial indexes, Python enables spatial data extraction, transformation, and loading (ETL) across massive datasets used in remote sensing and satellite data analysis and GIS spatial data engineering.
My technical stack integrates both open-source libraries (GeoPandas, Shapely, PyProj, Rasterio, GDAL/OGR) used in QGIS spatial analysis and proprietary frameworks (ArcGIS Pro and ArcPy automation), feeding clean data directly into Web GIS interactive mapping portals.
Core Python Spatial Libraries & Frameworks
GeoPandas & Shapely Vector Operations
Spatial joins, geometric predicates (intersections, containment, touches), R-Tree spatial indexing, and on-the-fly coordinate reference system (CRS) transformations across millions of records.
Rasterio & NumPy Multi-band Raster Math
Reading multidimensional GeoTIFF and NetCDF imagery as NumPy arrays. Calculating zonal statistics, spectral indices (NDVI/NDWI), and running windowed reads without loading full rasters into memory.
ArcPy Enterprise Automation
Building automated Python Toolboxes (.pyt), geodatabase administration cursors (arcpy.da), and automated map production series using arcpy.mp.
GDAL/OGR & Cloud GEE API Integration
Executing high-speed C++ backed raster conversions (gdalwarp) and querying assets via Google Earth Engine remote sensing analysis.
Batch Satellite Analytics & Municipal Zonal Aggregation
Engineered custom Python scripts to parse time-series satellite imagery, compute vegetative stress metrics across administrative ward polygons, and output structured GeoPackage results with zero manual GIS interaction.