Lmst

Have an excellent long weekend everyone (if you are in the US), we all deserve it!
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H/T Simon Ilyushchenko for the brilliant image!
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#GIS #spatial #mapping #raster #mapnerds #mapgeeks #fridayfeeling #fridayfun #raster #projection #connected #gettingyourbearings #coordinates

MVT, MBT, PMTiles: Everything to know about vector tiles: @djh offers a comprehensive overview of #vectortiles technology, including #Mapbox Vector Tiles (#MVT), #MBTiles, and #Protomaps’s #PMTiles. The post explains the evolution from #raster to #vector #maps, compares key formats and technologies,...
https://spatialists.ch/posts/2025/05-19-mvt-mbt-pmtiles-everything-to-know-about-vector-tiles/ #GIS #GISchat #geospatial #SwissGIS

In the last post, Tis Veugen suggested dithering with the Gosper curve. At first I thought this doesn't make sense, since the curve lives on a hexagonal lattice, while dithering is generally done at the native pixel level, which means a square lattice. But as I thought about this further, it started to look like a fun challenge. Besides, the square grid hasn't always been the native way to organize pixels; for example, some old CRTs also used a hex lattice.

So I was really just making up excuses for the extra work. First I had to set up interpolated sampling, which would come for free in OpenGL, but now I was working on the CPU. The Gosper curve was also new to me, and I implemented it in my own way from first principles using IFS ideas, like I'd done earlier with the Peano curve.

For comparison, here's also a version with the boustrophedon curve on a hexagon, since I already had that curve function in my toolbox. Finally there's also the raw Gosper curve, though a smaller version to give a clearer idea.

#dithering #halftoneart #raster #pixelart #gospercurve #planefillingcurve #spacefillingcurve #pythoncode #opengl #algorithmicart #algorist #mathart #laskutaide #ittaide #kuavataide #iterati

The previous demo made me dig deeper into dithering algorithms. It's something I should have done years ago, as I've been using simple random dithering now and then, and I hadn't even thought of gamma correction. One algorithm in particular caught my eye: Riemersma dithering, which uses the Hilbert curve. Compared to the usual matrices for error diffusion, the curve approach seemed easier to implement in some ways, as it has fewer edge issues.

More interestingly, it struck a chord with my earlier experiments with space-filling curves in image processing. So it was a kind of familiar territory, but it also seemed esoteric enough that I could imagine making some new discoveries. For example, play with other plane-filling curves besides the Hilbert.

The first image uses the boustrophedon curve, which makes the vertical wave patterns I recall from a number of non-dithering demos. The second curve is what I call the diagstrophedon, a diagonal zig-zag starting from the top left corner, and I think its wavy artefacts make a nice match for Venus's hair.

Then in image 3 we have Hilbert, which doesn't seem to make any particular artefacts, and I guess that's a good thing for dithering. Finally 4 uses the Peano curve, which makes some fun wiggles in light areas.

#dithering #riemersmadithering #stippling #halftoneart #raster #pixelart #hilbertcurve #peanocurve #planefillingcurve #spacefillingcurve #pythoncode #opengl #algorithmicart #algorist #mathart #laskutaide #ittaide #kuavataide #iterati

Yet another optimized Voronoi tiling. This was supposed to be a simpler alternative to the previous tiling approach, but I ended up going down a rabbit hole of various dithering algorithms.

Instead of the equal-sum partitions, this uses regular dithering to find the Voronoi seed points, and the tile areas are inversely proportional to the colour values. My simple random dithering didn't work as well as expected, so I started to look into alternatives. I was of course aware of Floyd-Steinberg, and as I grew a bit tired of this demo, I ended up cheating a little by using a ready-made library. Since I was using PIL for image handling anyway, I could simply use its format conversion functions to get a 1-bit image, which uses F-S by default.

As before, I planned to use edge detection to get smaller tiles at the contrast edges. However, it didn't work so well with dithering, as it would concentrate basically all seed points at the edges. So here I used a mix of edge detection and the original. I think it worked out nicely here, as the bright colours often coincide with areas of small detail, while the dark background doesn't need much resolution.

#voronoidiagram #pixelart #blockart #tiling #raster #mosaic #pythoncode #opengl #algorithmicart #algorist #mathart #laskutaide #ittaide #kuavataide #iterati

I wanted to make the optimized tiling look more organic, and I've already played with Voronoi diagrams quite a lot. But this turned out a bit more messy than expected. To preserve the size and shape variations of the rectangles in the Voronoi cells, I ended up using a non-uniformly weighted metric. I guess it counts as a shape metric, using the definition in my Bridges 2020 paper.

As before, there's a roughly uniform tiling with an equal number of cells for comparison.

#voronoidiagram #pixelart #blockart #tiling #raster #mosaic #pythoncode #opengl #algorithmicart #algorist #mathart #laskutaide #ittaide #kuavataide #iterati

Weer #poëzie uit tijdschrift #Raster dit weekend. Halverwege de jaren '80 werd de balans opgemaakt van de stand van zaken in de Nederlandstalige poëzie. Veel dezelfde dichters als een kleine 10 jaar daarvoor komen voor in het tijdschrift. Vandaag Kouwenaar en Tentije.

Día 8 | Distribuciones – Histograma | #30DayChartChallenge. | Visualización hecha usando R con los paquetes #ggplot2, #dplyr, #patchwork, #sf, #ggtext, #showtext, #raster, #exactextractr, #ggscale y #scales.

Another visual refinement to a recent demo: adding seams to the optimized tiling scheme. While it breaks the mosaic illusion to some extent, I think it makes the tile sizing idea much clearer. As before, there's a uniform variant with the same number of tiles for comparison.

Source photo: Mary of Egypt by José de Ribera, in public domain.

#pixelart #blockart #tiling #raster #mosaic #pythoncode #opengl #algorithmicart #algorist #mathart #laskutaide #ittaide #kuavataide #iterati

Día 9 | Distribuciones – Divergente | #30DatChartChallenge. | Visualización hecha usando R con los paquetes #ggplot2, #dplyr, #patchwork, #sf, #ggtext, #showtext, #raster, #exactextractr and #SPEI.

RiverREM - Generating River Relative Elevation Model (REM) Visualisations
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https://github.com/OpenTopography/RiverREM <-- shared GitHub repository
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https://opentopography.github.io/RiverREM/ <-- shared documentation
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https://opentopography.org/blog/new-package-automates-river-relative-elevation-model-rem-generation <-- shared Open Topography blog post about RiverREM
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H/T Heidi Luchsinger / Open Topography
[this post should not be considered as an endorsement of this product aka caveat emptor]
#GIS #spatial #mapping #remotesensing #earthobservation #Python #LiDAR #RiverREM #visualisation #hydrology #water #river #rivervalley #floodplain #terraces #GitHub #opensource #opendata #DEM #REM #model #modeling #RiverRelativeModel #documentation #tutorial #learning #tool #elevation #raster #sinuosity #drainage

Day 10 | Distributions / Multi – Modal | #30DayChartChallenge. Visualization made with R using #ggplot2, #tidyverse, #cowplot, #stars, #raster, #ggspatial and #sf. Data source: Sentinel-2 MSI (2024)

This is a TLDR of the presentation I did at #foss4g in Belém, last year.

Check it out: https://george-silva.hashnode.dev/efficient-methods-for-generating-zonal-statistics-in-gis

#gis #remotesensing #raster #python #rasterio #postgis #postgresql

New Map Of Landscape Beneath Antarctica Unveiled
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https://phys.org/news/2025-03-landscape-beneath-antarctica-unveiled.html <-- shared technical article
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https://doi.org/10.1038/s41597-025-04672-y <-- shared paper
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#GIS #spatial #mapping #Bedmap3 #icebed #surface #thickness #gridded #datasets #Antarctica #raster #model #modeling #landscape #elevation #icesheet #survey #remotesensing #earthobservation #climatechange #warming #climate #melt #melting #seafloor #subglacial #geophysical #survey #topography #geology #bathymetry #topobathy #BritishAntarcticSurvey
@BritishAntarcticSurvey

schematic / flow chart - Schematic showing the Bedmp3 source datasets (white boxes) combining to make intermediate products (blue boxes) and ultimately the final set of grids (orange boxes) and their uncertainties (yellow boxes). Note that the surface grid has a uniform estimated uncertainty (Section Uncertainty estimates).

maps - Bedmap3 grids of (a) bed topography and (b) surface elevation, in metres above sea level (g104c geoid), and (c) ice thickness in metres. Locations labelled in (b) are referred to in the text

The BedMachine v3 bed has some subjective advantages over Bedmap3 where glacier flow is relatively fast, but survey data are sparse. (a) Surface flow speed43; (b) BedMachine v3 bed topography; (c) Bedmap3 bed topography, both overlaid on shaded relief and with survey data shown as grey lines. The black dotted line highlights a trough under slow-flowing ice that is more smoothly continuous in Bedmap3. The white oval highlights the trough of a relatively fast-flowing ice stream that in BedMachine v3 is more smoothly streamlined and subjectively more realistic than in Bedmap3, given the typical streamlined form of deglaciated ice stream landscapes. An objective test of bed accuracy in this area requires more survey data.

maps - The Bedmap3 survey dataset allows us to calibrate the more continuous and extensive freeboard-based ice shelf thickness grid. (a) Pointwise ice thickness biases between 119,000 survey measurements and the freeboard-derived ice thickness grid47 on the Ross Ice Shelf, showing coherent patterns of bias; (b) these pointwise offsets median-filtered over a 20 km radius; (c) spline-interpolated calibration grid of the filtered offsets in (b); (d) bias-corrected version of the freeboard-derived ice thickness grid after subtraction of the calibration grid in (c).

Ever needed to reuse the same raster extent & resolution across multiple QGIS tasks? My latest blog post shows how the Raster Layer Properties function can automatically grab all those details from a reference layer. A good way to make your workflows more consistent and error-proof. https://ecodiv.earth/post/rasterlayerproperties/ #QGIS #GIS #FOSS #Raster #OpenSource