Jakub Nowosad🌍 Blog series: Spatial Machine Learning with R
From caret to tidymodels, mlr3, and specialized spatial ML packages — explore how spatial context changes the way we build ML models in R.
Start with Part 1 👉 https://geocompx.org/post/2025/sml-bp1/
🚨 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/
🚀 New blog post! Part 5 of our series on spatial ML with #RStats explores specialized packages: RandomForestsGLS, spatialRF, and meteo -- tools beyond caret, tidymodels, & mlr3.
New blog post by Jan Linnenbrink: Spatial machine learning with caret 📍
Using `caret` to predict air temperature in Spain with spatial data, addressing autocorrelation and extrapolation with `blockCV` and `CAST`.
Read here: https://geocompx.org/post/2025/sml-bp2/
It’s been 8 months since I started my MSCA-PF fellowship 🇩🇪
Read the latest blog post for updates on research, collaborations, and life in Münster.
The PRISM project’s progress report: 8 months into the MSCA-PF journey – Thinking in spatial patterns
Hi! I’m Jakub Nowosad, an Associate Professor at Adam Mickiewicz University and a Visiting Scientist at the University of Münster. My work focuses on developing spatial methods to better understand environmental processes. Here, I present my research, publications, and geocomputational projects.
🚀 Last week, we hosted "Advances in Spatial Machine Learning 2025", a two-day workshop tackling open challenges in the field.
From validation to uncertainty analysis, we explored key topics with top researchers.
Now, we're working on synthesizing outcomes—stay tuned!
#SpatialML #MachineLearning #Geospatial #DataScience #DeepLearning #GISchat
Want to showcase your work on #MachineLearning in #Geography? 🌍
Submit to Erdkunde's Special Issue: Machine Learning in Geography – Challenges & Perspectives.
🔗 More info: https://buff.ly/3VLlPGq
🛰️ A new paper "scikit-eo: A Python package for Remote Sensing Data Analysis" on a tool for #LULC analysis with various machine learning and neural networks algorithms.🛰️
Article: https://doi.org/10.21105/joss.06692
Software: https://yotarazona.github.io/scikit-eo/
My #ECEM23 talk is today (Sep 5th) at 11:30 AM in Hall 1 B.
"Exploring spatial autocorrelation and variable importance in machine learning models using
patternograms"
