Global Vector Terrain - Speaker Deck

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Designing global hillshades & landcover as vectors AJ Ashton, Mapbox

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Intro What is Mapbox Vector Terrain?

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3 layers Elevation Contours Hillshading Generalized landcover

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Vector tiles ● Same location & numbering scheme as web mercator image tiles ● Contain points, lines, polygons, & data fields ● Designed to be turned into images very quickly with any custom stylesheet ● OpenGL / WebGL provide even more speed & flexibility

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Global coverage ● 25+ raw data sets compiled into one ● 45,000 raw data files

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Global coverage ● USA: US Geological Survey, NASA ● Canada: Geobase ● Mexico: INEGI ● Norway: Kartverket ● France: IGN ● UK: Ordnance Survey ● Denmark: Geodatastyrelsen ● New Zealand: Geographx ● +More

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The process, roughly: ● Gather the best elevation & landcover raster data sources we can ● Normalize & mosaic the various sources, prioritizing by highest resolution ● Generate raster hillshade & landcover visualizations ● Programatically trace vector polygons from the processed rasters

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Built on open-source GDAL, PostGIS, Mapnik, GraphicsMagick ⬇ managed by custom Bash & Python scripts

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Powered by Amazon Web Services ● 9,800 jobs managed by Simple Queue Service ● Parallel-processed across 250 Elastic Cloud Compute (EC2) virtual machines ● Vector tiles uploaded to Simple Storage Service (S3)

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Hillshading Process & design

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Classic approach at the foundation ● Standard GDAL hillshading algorithm ● Traditional lighting from the northwest ● Raster input, raster output

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Accounting for scale ● DEMs resampled for each zoom level 2-14 ● Hillshading vertical exaggeration set appropriately for each zoom level

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Accounting for Web Mercator ● Mercator 'meters' are only simlilar to real- world meters near the equator ● At 60° north or south, 2 mercator meters fit into one real-world meter ● This affects slope & hillshade calculations

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Accounting for Web Mercator

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Accounting for Web Mercator Horizontal scaling = 1 / cos(latitude)

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Default scaling (none) Default scaling (none)

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Latitude-appropriate scaling Latitude-appropriate scaling

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Raster to vector

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Raster to vector gdal_polygonize.py with a little help from GraphicsMagick

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Landcover Process & design

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Similar to hillshade process ● Start by normalizing raw data sources ● Reduce rasters to a small number of colors ● Trace with gdal_polygonize.py ● Simplify to remove pixelization

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Elevation Contours Process & design

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Straight-forward ● Start with the same DEMs we processed for hillshading ● Generate contours with gdal_contour ● ✔

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But why vector terrain?

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References & Resources ● shadedrelief.com by Tom Patterson ● reliefshading.com by Bernhard Jenny & Stefan Räber ● Cartographic Relief Presentation by Eduard Imhof ● gis.stackexchange.com posts by William Huber & many others

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mapbox.com/mapbox-studio