How can we democratize the development of geospatial machine learning models, lower the barrier to entry for students and practitioners in this space, and obliterate the ‘practice’ of geospatial platform commercialization? Leveraging OSM vector data and cloud compute, through such programs such as the University of Washington’s GeoHackweek, we are able to further the removal of the knowledge barrier for scaling ML applications and flood a commercialized marketplace with models leverage-able by a broader community. If theoretically coupled with virtual (or real) incentivization or enhanced social currency, this approach could advance stagnant geospatial activities and create a community invested in producing optimal solutions that become foundational to advanced endeavors.
Mapping Brands with the Name Suggestion Index
The Name Suggestion Index is a collection of over 4000 branded businesses that volunteers have identified in OpenStreetMap and matched to Wikidata IDs. Learn how we built this project, and how it...
