OpenStreetMap US

With the help of a diverse community of extremely motivated locals, we’ve collected high-quality POI data across NYC, Austin, LA, and counting. We’ve experimented with financial incentives, intrinsic rewards, and feedback mechanisms to try to hone in on the most accurate and complete POI dataset in the world. We’re still working towards that goal and would love to share what we’ve learned so far. We also believe that this type of data should be open and accessible to ensure success in the long run. We’re experimenting with various concepts in this realm as well and are excited to share our ideas on how our work can strengthen and grow OpenStreetMap data and community!

Next up in State of the Map US

Previous talk

How much is enough?: A deep dive on training data requirements for mapping with deep learning

Sep 7, 2019 · Daniel Hogan

Deep learning algorithms offer the potential to rapidly extract foundational mapping information directly from satellite imagery. Such algorithms can map regions of interest far faster than human labelers (making them especially valuable...