Michael Sumner mdsumner

Myriahedral Projections: Implementations

Myriahedral projections (van Wijk, 2008) are not projections in the PROJ sense — there's no forward/inverse formula. They're a mesh-algorithm-projection pipeline:

Build a fine triangular mesh on the sphere (icosahedral subdivision, graticule grid, or geography-adaptive)
Assign edge weights (land/ocean crossings, graticule alignment, etc.)
Compute a minimum spanning tree of the dual graph to decide where to cut
Unfold the tree of faces flat using per-face gnomonic projections

The result has negligible area and angle distortion at the cost of many interrupts.

GDAL-ready basemap tiles

Access common web basemap tile services via GDAL. Returns either WMTS connection strings (for ESRI services with WMTS support) or TMS minidriver XML for XYZ tile services.

Function

basemap <- function(name = NULL, url = NULL, api_key = NULL, tile_level = 19L, bands = 3L) {
  providers <- list(
    # OpenStreetMap (no key)
    OpenStreetMap = "https://tile.openstreetmap.org/${z}/${x}/${y}.png",

library(wk)
library(geos)
library(PROJ)
## flatten all to POLYGON
map <- wk_flatten(as_wkb(rnaturalearth::ne_countries(scale = 50)))
local_laea <- function(x, merctrans) {
  cent <- geos_centroid(x)
  mercxy <- wk_coords(wk_transform(cent, merctrans))[,c("x", "y")]
 xy &lt;- wk_coords(cent)[, c("x", "y")]

Caching Range Requests: What GDAL Already Does (and the Hard Part It Doesn't)

The question of how to cache HTTP range requests — particularly for cloud-native geospatial formats like COG, PMTiles, FlatGeobuf, and cloud-optimized GeoParquet — keeps coming up. Brandon Liu's How many ranges can you fit in one request is a good treatment of the multi-range packing problem. But there's a mature, battle-tested system that already handles much of this at the client level, and its design choices are instructive even for people building entirely different stacks: GDAL's /vsicurl/ virtual filesystem.

GDAL's VSI curl layer and caching

When GDAL reads a cloud-optimized file via /vsicurl/ (or its cloud-specific variants /vsis3/, /vsigs/, /vsiaz/), it performs range request management internally. The behaviour is controlled by a set of configuration options that most users never touch, but that encode a lot of hard-won knowledge about how to read efficie

this is fine

Investigate changes required to add numpy.datetime64 support to GDAL Zarr driver

https://raw.githubusercontent.com/mdsumner/gdal-r-python/refs/heads/main/gdal-issues/test-gdal-zarr-v3-datetime64.sh

https://raw.githubusercontent.com/mdsumner/gdal-r-python/refs/heads/main/gdal-issues/zarr-v3-numpy-datetime64.md

Guidelines for PR fix

A PR to fix this will need

https://fosstodon.org/@hughagraham/116005729038262380

## generate a tif with a nodata collar
dsn <- "/vsicurl/https://projects.pawsey.org.au/idea-gebco-tif/GEBCO_2024.tif"
library(terra)
#> terra 1.8.96
pr <- rast(ext(c(-1, 1, -1, 1)) * 1e7, res = 4000, crs = "+proj=laea")
pr

	##' ESRI Basemap Services
	##'
	##' Access ESRI ArcGIS Online basemap services via WMTS (preferred) or TMS XML fallback.
	##'
	##' @param name Character. Name of the basemap service. Use `esri_list()` to see available options.
	##' @param url Character. For custom services, provide the MapServer base URL
	##' (e.g., "https://services.arcgisonline.com/arcgis/rest/services/Ocean_Basemap/MapServer").
	##' @param tile_level Integer. Maximum tile level for TMS XML (default 19).
	##' @param bands Integer. Number of bands - 3 for RGB, 4 for RGBA (default 3).
	##'

	#!/usr/bin/env bash
	# test_gdal_zarr_v3_datetime64.sh
	#
	# Reproducer for GDAL Zarr V3 numpy.datetime64 extension data type issue.
	# Creates a minimal local Zarr V3 store with a datetime64 time coordinate,
	# then exercises GDAL classic 2D raster API access patterns to confirm
	# which operations are affected by the unsupported extension type.
	#
	# Requirements:
	# - GDAL >= 3.8 (with Zarr V3 support)