[celoyd]

I wrote that Mapbox post, and helped with some of the processing that happens to get this data on AWS. Yep, you get multispectral resolution of 30 m, and with pansharpening (http://en.wikipedia.org/wiki/Pansharpened_image, etc.) you can get visually acceptable quality at 15 m in RGB.

Landsat is basically intended for science about seasonal/annual/decade-scale changes in Earth’s land surface. When you see an estimate of how a city’s built-up area has grown since 1980, or how the Everglades are changing, it probably has Landsat as one source. This explains a lot of design decisions that might seem weird to a layperson who wants to use it for everyday RGB imagery. Most use of Landsat imagery is basically off-label. It’s just very good data in terms of accuracy, precision, and general ease of use. And if I say so myself, it looks real pretty: https://www.mapbox.com/blog/landsat-live-live/

[sjtrny]

Which pansharpening method is used in the example image?

[celoyd]

In the images in the blog post and the live map? Those aren’t pansharpened at all. If we do add pansharpening in a later version, it’ll likely be naïve, without spatially aware modeling of the multispectral data. (Specifically, it’ll probably be a cleaned-up, rasterio-based, null-aware, parallelized descendant of this sketch of the Brovey transform in numpy: https://gist.github.com/celoyd/2e7beed82951d22b9b90 .)

From what I’ve seen – and I haven’t tested it carefully yet, so I could be wrong – the more elaborate methods are severe overkill on Landsat 8. It has only 4 pan px per multi px (where some commercial data has 9 or 16), and the pan band is almost exactly R+G+B (without NIR). So my gut and some simple experiments suggest that doing PCA-or-whatever is overthinking it.