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Working with OpenStreetMap using Apache Spark a...

Working with OpenStreetMap using Apache Spark and GeoTrellis - SotMUS 2018

Seth Fitzsimmons

October 07, 2018
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  1. What if you wanted to query all of OSM for

    all time (for every change ever)?
  2. We need a new name! Think of suggestions and at

    the end the talk we will vote!
  3. OSMesa is a set of Spark functions to churn through

    enormous quantities of weird data and produce useful results quickly.
  4. Philosophy: Make data accessible from standard tools OSM PBF -

    supported by OSM-aware tools ORC - supported by the Hadoop ecosystem OSM data model - (partially) supported by OSM-aware tools OGC data model - supported by everything else
  5. OSM Data Model The OSM data model consists mainly of

    3 elements: • Nodes - Points • Ways - LineStrings, Polygons • Relations - GeometryCollections, Polygon with holes, MultiPolygons As well as the tag-based metadata that applies to each elements, and changesets grouping edits
  6. OSM Data Model: Changesets • Edits are grouped into changesets,

    which have their own metadata such as use comments (for developers, think commit messages) • Adding hashtags to user comments allows downstream processing to group changes - for example, #HOTLunch
  7. • With OSMesa, we can create full historical geometries. •

    To do this, we need needed to create a concept of “minor versions” of geometries • We converted timestamp to an update date that propagates up to the way or relation • We added a “valid_until” tag on elements that tells when an element is no longer valid (either replaced or deleted) Creating features from History
  8. way v1 highway=unclassified node v1 node v1 node v1 node

    v1 node v1 node v1 node v2 node v2 way v2 highway=primary node v1 node v1 node v2 node v2 way v1 highway=unclassified
  9. way v1 highway=unclassified node v1 node v1 node v1 node

    v1 way v1.1 highway=unclassified node v1 node v1 node v2 node v2 way v2 highway=primary node v1 node v1 node v2 node v2 minor version change
  10. • With minor versions, we can bake new ORC files

    that contain geometries of every element in OSM history, with ways/relations representing every edit to the element as well as elements that they contain • Then, we compute statistics per changeset based on geometries, and roll up the statistics per user and hashtag Full historical geometries
  11. • Processing of full history into features in under 40

    minutes (cluster of 255 m3.2xlarge nodes) • This is not a small cluster ( ≈$65/hour). YMMV with smaller clusters. • We are building update mechanisms to avoid refreshing the entire dataset • Produces 600GB of ORC Processing OSM data at scale
  12. Other Features • Streaming updates of vector tiles based on

    replication files (Spark Streaming) • Streaming aggregations
  13. • Building matching between OSM and other vector datasets •

    Generating vector tiles for URCHN containing a subset of historical data to front-end analytics OSMesa: Other current uses
  14. The Future: Validation workflows, Reputation scores • Better validation workflows

    is a big question in the OSM community right now (according to SOTM US 2017) • HOT Tasking manager does some; we can do better • One way to improve validation workflows is to suggest validation be done by veteran mappers, validation be suggested for more junior mappers (“reputations core”) • Development Seed, who contribute & uses OSMesa work, have great ideas in this space.
  15. The Future: Machine Learning pre- and post- processing • Pre-processing

    geospatial imagery and OSM into training chips - a distributed label-maker • Managing data into and out of Raster Vision • Post-processing by cleaning the model output, matching to OSM or other vector data to remove duplicates, conflation workflows • Matching OSM to imagery dates: e.g. pre- and post- disaster.
  16. THANKS! Rob Emanuele, Azavea @lossyrob (Twitter, GitHub) www.azavea.com Seth Fitzsimmons,

    Pacific Atlas @mojodna (Twitter, GitHub) www.pacatlas.com github.com/azavea/osmesa One more cool visualization: https://vimeo.com/269953189