Running pyccd on larger areas

[caffeine-potent]

I'm working on generating a "change-density" heat-map visualization using pyccd. Each pixel in this map is assigned a value equivalent to the number of change-models generated for that pixel using pyccd.

Here is a profile of my results:

• 15 years of landsat imagery (~220 acquisitions)
• 10km^2 extent (~300px ~300px)
• takes approximately 2.1 hrs on 8 cores
• single pyccd process takes .18 seconds (or 180 ms)

I was wondering what strategies USGS-EROS uses to run pyccd based analysis on larger areas?

[klsmith-usgs]

I am unable to give out too many details at this time, but those strategies are being fleshed out more during our algorithm evaluation.

Sorry!

[123helloworld123]

when i am running this code: https://github.com/USGS-EROS/lcmap-pyccd/blob/develop/notebooks/ccd_example.ipynb
I find i can't get the same result as you showed in this page. I am wondering why i can't get the same result even i use the default test data you used?

Thanks