Geospatial QA/QC: Vector and Raster Validation
In the QA/QC section, I was responsible for conducting comprehensive quality assurance and quality control of geospatial datasets using industry-standard software such as Global Mapper, ArcMap, and MicroStation. My work focused on both raster and vector data, ensuring data accuracy, completeness, and seamless integration across platforms. I performed detailed QC on Digital Elevation Model (DEM) files, checking for the accurate representation of buildings, bridges, break lines, water surfaces, and tree canopy. I also identified and addressed issues such as corrupt or incomplete files and ensured correct clipping of data to project boundaries.
In addition to DEM validation, I carried out raster imagery QC, where I examined the quality of the image tiles to detect misclassified LiDAR points, incomplete data coverage, and distorted image sections. These discrepancies were carefully documented and communicated with the processing team, who implemented the corrections to ensure seamless and accurate final datasets.
Example showing multiple polygons representing a single feature.
Example showing one polygon representing multiple features
Example showing irregular polygons
I was also involved in QC of LiDAR point cloud data using MicroStation. My focus was on verifying the accuracy of point classifications for features such as ground, buildings, roads, and vegetation. I identified and corrected misplaced points and ensured that the surface models were smooth and free from classification errors. This work directly supported the generation of reliable topographic and terrain models for engineering and planning use.
On the vector data side, I reviewed building footprints and other spatial layers for positional and attribute accuracy. I replaced corrupt files, digitized missing polygons, and created new vector features as needed. For files with a high concentration of issues, I created a dedicated “issues” shapefile to document the problems and flag them for further analysis and rework. Throughout the QA/QC process, I maintained a strong focus on data quality and consistency, contributing to the successful delivery of clean, accurate, and usable geospatial datasets.
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