New R package: rgeomorphon 
by Andrew Brown
Classifies terrain forms using a parallel C++ implementation of the geomorphon algorithm.
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#rspatial
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Have you read Geocomputation with R?
If you found it helpful, we’d really appreciate a quick review — it helps others find the book too!
Goodreads: https://www.goodreads.com/book/show/214736719-geocomputation-with-r
Amazon: https://www.amazon.com/dp/1032248882
Book: https://r.geocompx.org/
Chapter 13: Transport Systems Analysis 
Model transport networks! 
Analyze travel patterns, desire lines, routes, and accessibility using open data and reproducible geospatial workflows in R.
New R package: forestdata makes it easy to download forestry and land cover data from multiple sources (Copernicus, ESRI, EU-Trees4F, and more). Supports sf, SpatRaster, and tidy outputs.
Explore it here: https://cidree.github.io/forestdata/
New CRAN release of gdalraster, R bindings to GDAL. v2.1.0 adds incremental new features and enhancements, minor bug fixes and several internal improvements:
https://github.com/USDAForestService/gdalraster/releases/tag/v.2.1.0
Vector read benchmarks: https://usdaforestservice.github.io/gdalraster/articles/vector-read-benchmarks.html
GitHubRelease gdalraster 2.1.0 · USDAForestService/gdalrasterFeatures / enhancements
Raster
pixel_extract(): support optional point IDs in the first column of an input data frame and add argument as_data_frame (#751)
performance improvement for plot_raster(...
New R package alert: mbg for model-based geostatistics
Run spatial ML & geostatistical models to estimate continuous surfaces from point data + raster covariates.
Built on sf, terra, data.table, caret, and R-INLA.
Final part of our Spatial ML with R series !
We explore spatial cross-validation with sperrorest & blockCV — tools outside the usual ML frameworks
URL: https://geocompx.org/post/2025/sml-bp6/
Okay - further to my early rants about CDSE data, it aint as bad as I thought 
it also prompted me to properly sort out my approach to scaling/offsets which had been driving me mad! So if anyone cares for another way to download data from CDSE, with #rspatial / #gdal I made a gist:
https://gist.github.com/h-a-graham/86cd3403445cf163ce958efa2d29c621
There are still some improvements to be made for sure.
FYI @Micha_Silver
Chapter 11: Writing Geoalgorithms 
Focuses on developing reusable and reproducible code for spatial tasks in R. Demonstrates algorithm design using examples like calculating polygon centroids.
The first major version of {mapsf} has arrived on CRAN!
mapsf is a thematic mapping R package. Its goal is to be simple and lightweight while offering all the necessary features to create beautiful statistical maps.
This release includes a revamped theming system, an updated cheat sheet and improved PNG and SVG exports and solves some long-lasting display bugs.
More details in this blog post: https://rcarto.github.io/posts/mapsf_v1.0.0
New website: https://riatelab.github.io/mapsf
A heads-up about the Geocomputation with R book: some copies were mistakenly printed in black & white instead of full color. If you received one, please contact me or the publisher for a replacement. A new, correct copy will be sent to you!
Publisher: https://www.routledge.com/contacts/customer-service
I finally manage to watch @paleolimbot presentation at @RConsortium on "scaling the #Rspatial ecosystem" !
https://www.youtube.com/watch?v=tjNEoIYr_ag
Quick subjective key points:
- Use the database Luke and learn a bit SQL (I was already converted)
- the diversity of R packages to do some workflows also represent the diversity of standards (s.f.) and steps to reach similar results
- wkt_filter seems very nice (I was using "query" and GDAL/SQL instead)
Chapter 10: Bridges to GIS Tools 
Shows how to connect R with external GIS tools like QGIS, GRASS, and SAGA. Also includes guidance on working with GDAL, spatial databases, and cloud-based services.
Replied in thread
@MichaelTBacon @eliocamp here it seems #Rspatial is innocent, #GDAL error is helpful here.
I should start collecting all the ways in which #RSpatial can go wrong spectacularly.
Quick trick: subsetting the BIOCLIM CHELSA dataset by the Swiss Federal Institute for Forest, Snow and Landscape Research WSL using Cloud Optimized Geotiff (COG) functionality in R.
This feature seems overlooked in the documentation but could save you orders of magnitude in time, bandwidth and disk space - depending on the use case.
https://bluegreenlabs.org/post/chelsa-cog-geotiff-subsetting/
BlueGreen Labs · CHELSA dynamic BIOCLIM subsets | BlueGreen LabsThe CHELSA dataset provides access to climatologies at high resolution for the earth’s land surface areas. Among those are the global climate-related predictors at kilometer resolution for the past and future (Brun et al. 2022) - generally known as bio-climatic variables. This dataset is provided through the main CHELSA website and links to a file download option which focuses on bulk file downloads.
However, it seems that the geotiffs provided are cloud optimized geotiffs (COG files).
For the win. When you discover that a dataset is (accidentally?) formatted as COG Geotiffs, and saves you hundreds of GB in downloads.
Continued thread
Despite my talk’s quite frankly rubbish title it was still nice being back at Bristol R Users meet-up in person for the first time since 2019. Many thanks to @mhl20 for the inspiration on the postcode voronoi polygons.
Slides here: https://chrisdnewton.github.io/postcodes
chrisdnewton.github.ioFun with Postcodes in R
Interested in spatial machine learning with R?
We compare caret, tidymodels, and mlr3 for spatial tasks — and show how their workflows differ.
Read it here: https://geocompx.org/post/2025/sml-bp1/
