Your Service Just Went Viral....
Press Coverage
Record Traffic
Options
Common Patterns
Crowdsourcing
Medium sized Data
Imagery
Crowdsourcing
Why Crowdsource?
Crowdsourcing
Crowdsourcing
Crowdsourcing
Crowdsourcing
Crowdsourcing
Crowdsourcing
Crowdsourcing
Crowdsourcing Demo
Crowdsourcing Demo
Crowdsourcing Demo
Styling, Caching, and More
Maps as a Platform
"Medium-Sized" Data
Big, but not "Big Data"
Maps Engine Platform
Batch Uploading
Styling
Custom Icons
Simple Sharing
Maps Engine API
Imagery
Fast Response Imagery
Fast Response Imagery...needs to be...Fast
Mantoloking, NJ - November 1st, 2012
Before
After
Upload. Tile. Serve. Share.
Step 1. Create new Compute Engine Instance
Step 2. Install Apache and UbuntuGIS
Demo - gdal2tiles.py on Compute Engine
Fast Response Imagery on Google Maps
Rolling Your Own Tiles Pros and Cons
Pros:
- Fast
- Can be parallelized
- Static
- Great for small areas
- Low barrier to entry if you know what you're doing
Rolling Your Own Tiles Pros and Cons
Cons:
- Not great for large areas
- Potentially expensive if content goes viral
- Masking and other imagery processing can be complicated
- You have to handle all permissions
- Will have overhead for offering WMS and other services
- Configuration can be difficult and take a lot of planning
- Can be difficult for novices to get started
So What About Large Areas?
USDA NAIP - 1m Imagery
USDA NAIP - 1m Imagery
How Much Data?
How Many Tiles to Native Resolution (Level 18)?
USDA NAIP 2D Demo
USDA NAIP 3D GME Demo
Common Patterns
Crowdsourcing
Medium sized Data
Imagery
Thank You!
