Open-Source Geospatial Engineering
QGIS is one of the premier open-source GIS platforms globally, offering a robust processing framework that natively integrates GDAL/OGR, GRASS GIS, and SAGA GIS alongside proprietary systems like ArcGIS Pro and ArcPy automation. In my professional workflow, QGIS serves as a central hub for vector topology cleaning, remote sensing and satellite data analysis, hydrological watershed modeling, and advanced map composition.
From plugin-driven workflows such as the Semi-Automatic Classification Plugin (SCP) for supervised satellite imagery classification to writing custom script toolkits in Python geospatial data processing (PyQGIS), I utilize QGIS for rigorous academic and municipal geospatial tasks.
Demonstrated QGIS Technical Proficiencies
Semi-Automatic Classification Plugin (SCP)
Automated Landsat/Sentinel pre-processing, region-of-interest (ROI) spectral sampling, signature analysis, and Maximum Likelihood classification for detailed LULC maps.
Terrain & Hydrology Processing (SAGA / GRASS)
Executing r.watershed, flow accumulation, sink filling, Topographic Wetness Index (TWI), and contour generation from raw SRTM and ALOS PALSAR elevation models.
Advanced Print Layout & Cartography
Designing publication-quality map series, dynamic scalebars, coordinate graticules, custom legend styling, and atlas generation for multi-sheet spatial reporting.
Geopackage & PostGIS Integration
Managing standardized OGC GeoPackages, establishing live DB Manager connections to PostgreSQL/PostGIS, and feeding layers directly into Web GIS interactive mapping portals.
Multi-Temporal Urban Expansion Case Study in QGIS
Utilized QGIS and SCP to classify multi-sensor satellite data across five epochs, computing transition matrices that quantified a 60.9% urban expansion across Ahmedabad.