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Week 12 Cartographic Fundamentals
Introduction
In UAS work, collecting good data is only half the job—presenting it clearly is what makes it useful. A drone can produce high-quality raster products such as an orthomosaic and a DSM, but those outputs do not become “real” mapping products until they are organized, analyzed, and displayed in a readable map layout. This lab built on earlier geospatial concepts by focusing on cartographic fundamentals in ArcGIS Pro and learning how to turn UAS-derived rasters into maps that communicate information accurately. A key idea was understanding the difference between an aerial picture and a proper map: a map must include scale, orientation, location context, and data source details so that others can interpret and trust the results.
Overview
This lab used the Wolf Creek (Wolf Paving) dataset to practice the full workflow of creating cartographically correct maps in ArcGIS Pro. I organized project files, loaded the orthomosaic and DSM, and prepared the rasters by building pyramids and calculating statistics for smoother display and better contrast. I then improved DSM interpretability by generating hillshade and using symbology and transparency to highlight surface relief. Finally, I created export-ready map layouts that meet standard cartographic requirements, including a north arrow, scale bar, legend (especially for GCP points), locator map when required, watermark, and clear data source/metadata notes (sensor, platform, altitude, pilot). One of the key deliverables was a shaded DSM map with a transparent DSM overlay, plus a locator map showing the Wolf Creek site’s location within Wisconsin.
Conclusion
lab showed that cartography is more than making maps look “nice”—it is about making UAS results accurate, clear, and useful for other people. In ArcGIS Pro, I practiced the full workflow: organizing project folders, keeping metadata consistent, loading and visualizing DSM and orthomosaic rasters, building pyramids and statistics for better display, and improving elevation interpretation by creating hillshades. The DSM and hillshade helped me understand height and surface relief, while the orthomosaic provided real-world context to explain why those height changes occurred (such as trees, buildings, or pavement). Most importantly, I learned how to turn raw imagery into final, export-ready map layouts by adding essential cartographic elements—north arrow, scale bar, legend, locator map, watermark, and clear data source notes. This matters in real UAS work because the map is often the final product a client or supervisor uses to make decisions, so the layout must communicate information quickly, correctly, and with confidence.
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