@dereklieu • Development Seed
Rapid urban change means your maps are probably wrong.
We built a tool for these questions. It's called Urchn, which is short for URban CHaNge.
We partnered with Radiant, formerly Digital Globe, and Azavea. (Radiant came up with the name)
Urchn starts with persistent change.
We do pixel analysis on time series Sentinel and Landsat imagery.
It works by examining three consecutive, cloud-free images.
If a change persists in all three, we look back at three prior images for confirmation.
This tells us where a change occurred, and when.
2017 02 09
2017 02 15
2017 02 20
2017 03 11
2017 03 16
2017 07 19
We also aggregate, by tile and admin boundary.
We're interested in a few use cases.
1. Identify where are official maps are out of date.
2. Identify where OSM is out of date.
Here are a few more ideas we're experimenting with.
1. Predicted building footprint area using machine learning.
2. Urban land-use classification
3. Whatever else you've got?
If you could design a tool to monitor urban change, what would it tell you?
Let's end on a game.
Identify everything that's changed between one image...
...and the next.
This is Sursum Corda.
Sursum Corda is a housing cooperative for low-income residents.
This is the future of Sursum Corda.
This is what it looks like now.
To me, understanding urban change means making local knowledge widely known.
We've love to explore what that means for you.