The process of manually mapping pedestrian path networks for OpenStreetMap is difficult due to the large amount of data required and the potential for error. We developed a tool leveraging and integrating different globally-available data types for proactive pedestrian path and network data generation and traversal analytics incorporating accessibility. Along with existing street network data, state-of-the-art computer vision techniques are employed to automatically infer massive pedestrian path network information for 6 cities on 2 continents. The inferred pedestrian path network data are represented in OSM JSON format and can be directly used in OpenStreetMap as a data layer, or used in downstream routing and analytic applications.
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Previous talkOpenHistoricalMap: Doing History
History is harder than it looks. While there may be one true set of facts, almost immediately after an event, facts start getting lost or confused. Research is harder than just looking...
