Charlie Loyd @vruba@everything.happens.horse

@kissane oh my god

@jenlowe This is probably weird to say, but you were one of the people I had in mind as a reader of the documentation.

@marcpfister It’s a real pleasure to work with data that’s both (1) good in itself but also (2) sensibly arranged and documented.

@nelson Thank you! And yeah, I do not want to become an amateur community manager ;)

@kyle Hey, I appreciate that!

The most important part of Potato for me is its colors. For more than a decade, in several workplaces, I’ve griped about how standard pansharpening renders colors. It’s been gratifying to show what I think is a better way.

So if you work with satellite imagery, I hope Potato makes good holiday reading. If you don’t, I hope it gets you interested. And if you’re hiring for skills shown in it (chewy, cross-disciplinary spatial/visual/etc. work), drop me a line.

There’s a lot in the repo, but I’m happy to say the core code is small and efficient. You can pansharpen several megapixels/second on an ordinary CPU, and somewhere > 10 Mpx/s on a gaming rig.

It's all licensed CC BY-NC, like its training data: Maxar/Vantor’s Open Data Program (h/t @marcpfister). This is imagery for disaster response, and a goal of Potato is to publicize that data, and similar data, and to encourage work that makes it easier to use.

It’s got a lot of moving parts and I wanted to make it intelligible to several audiences, so the documentation is a bit much at times.

I’m not expecting most people to know what pansharpening is, or to have a pressing need to do it themselves. But it’s an interesting problem, and I hope it’s interesting to learn about. It’s taken a lot of inspiration from across domains (by movie colorists, for example).

I’ve been working on this occasionally for years. It’s been a weekend morning here, a notebook page on the bus there, week-long sprints between contracts, etc. Eager to learn what hugely embarrassing bugs I’ve left in it.

I’ve just published Potato, a new pansharpening package. It aims to render certain kinds of satellite imagery more clearly and accurately than what’s for sale and on satellite maps today: https://github.com/celoyd/potato/

It’s under 50k parameters, comes with a working checkpoint, runs on a home computer, and is specialized on WorldView-2/3 ARD imagery. It does a few things I haven’t noticed before in the literature: for example, using all the visible multispectral bands to make its visible colors.

@maxfenton I am rich in a type of digital asset known as FRIENDSHIP, Max. 🥰

It’s called the Getty Museum open data because it lets you get the wait no because you can freely be getting the no hang on okay because you can museum the gotten no I messed it up again but seriously folks you can “got” the artworks aw shit
https://mastodon.social/@creativecommons/115532158945263774

Technically this is a type of phase diagram.
https://pixelfed.social/p/Atanas/895754272486310141

Everyone’s going to be grateful for my explanation of satellite imagery data pathways if I make it look like a category theory diagram without actually using the notation correctly!

Decades from now, some relative of mine is going to be going over the old notebooks I willed to them, flip one open to the page where I doodled “Turgeneverending Story Musgrave of the Fireflies” and put them all in the nonrecyclable trash.

@tmcw Or more personally, things I use (and recommend) today that I didn’t 3 years ago: Obsidian, uv, tailscale, LSP, duckdb.