Charlie Loyd @vruba@everything.happens.horse

@dmahr That’s a great question and probably actually a good way to explain this one day when it’s more complete: “See how this distribution, in naïve WGS 84, is visibly stretched compared to this other distribution that knows about drift?”

@migurski I suspect hardware maintenance but I’m not sure.

So I think I’m borderline resolving the continental drift signal with $50 of hardware under a roof.

This makes me happy because it’s pretty comparable to the trend from a nearby science-quality GNSS receiver: https://sideshow.jpl.nasa.gov/post/links/EBMD.html

But it’s been a year, so I tested the main thing I wanted to see. If I aggressively remove outliers, average across long time periods, and eyeball a few load-bearing parameters just right – so I want to be clear that I’m not claiming rigor, only that it’s good enough for me personally – the data shows a velocity of 27.5 mm/year west, 14.4 mm/year north. (This is on the order of 1 nanometer/second.)

Its output goes straight to a postgres database on a small always-on computer. Its SSD failed a few months ago, so I lost a chunk of data, but basically it’s just recording the chip’s output. Under my roof, you might note.

One of them has been sitting on the top of the curtain rod over my desk, carefully wedged in place with magnets and a makeshift ground plane.

Last summer, I got a couple of these little USB GNSS chips, a notch above the usual consumer quality but a notch (and $200) below the RTK-capable kind: https://mou.sr/4hrR4PY

Somewhat bananas suggestion that there’s an ancient Zoroastrian necropolis in inland Madagascar: https://www.tandfonline.com/doi/full/10.1080/0067270X.2024.2380619

@ian @kgjenkins Yes, I think (70% sure) these are plastic carrier bags full of old roofing.

@geowurster As usual, @sgillies has anticipated everything I could possibly have wanted.

@geowurster I guess this is what geolocation arrays are for, through. Obviously it’s my first time dealing seriously with this style of georeferencing.

@geowurster The thing is that it’s so oblique at the edges that the terrain correction baked into the GCPs (or whatever the better name is) matters quite a bit. So the common sense thing would be to discard most of them, but I’d rather not do that. Therefore it’s order 1e6 GCPs, which feels like a lot to ask even GDAL, but I haven’t tested!

@geowurster So I sort of talked myself out of GDAL at the beginning of thinking about this, but maybe it’s been the obvious choice all along.

I’m just trying to reproject PACE OCI L1B data, which does the weather satellite thing of having bands for longitude and latitude values, so it’s essentially one GCP per pixel.

The magic thing about python’s sys.exit() is you don’t have to worry about importing it; if you forget, the script exits when it gets there almost exactly as if you had.

I want to map from a small input to a large output with interpolation in the output and I’m going to be able to figure it out but it’s going to feel like doing my taxes the whole time.

I have come to a place in some personal-project code where I need to use skimage.transform.warp and I keep tabbing over to the documentation, sighing, and tabbing away.

This @kissane talk is worth your time if you’re interested in pretty much anything I’m interested in. https://xoxofest.com/2024/videos/erin-kissane/