#spatialml — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #spatialml, aggregated by home.social.
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🚦 New workshop from #ML4EO2026: "Where your models can be trusted"
Learn how to evaluate spatial ML using kNN distance matching CV, Area of Applicability (AoA), and more. All with R examples.
Materials: slides, code, data, hands-on exercises are at https://jakubnowosad.com/ml4eo2026workshop/.
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🚦 New workshop from #ML4EO2026: "Where your models can be trusted"
Learn how to evaluate spatial ML using kNN distance matching CV, Area of Applicability (AoA), and more. All with R examples.
Materials: slides, code, data, hands-on exercises are at https://jakubnowosad.com/ml4eo2026workshop/.
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🌍 My #ML4EO2026 talk focused on spatial machine learning and prediction-domain adaptive evaluation: defining the prediction domain, adapting validation, and weighting evaluation by deployment location properties.
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🌍 My #ML4EO2026 talk focused on spatial machine learning and prediction-domain adaptive evaluation: defining the prediction domain, adapting validation, and weighting evaluation by deployment location properties.
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🌍 New paper on key challenges in spatial machine learning!
We discuss six major topics shaping the field: model validation, performance metrics, uncertainty quantification, algorithmic diversity, software implementation, and modeling protocols. The paper highlights persistent challenges around robust evaluation and reproducibility in geospatial ML workflows.
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🌍 New paper on key challenges in spatial machine learning!
We discuss six major topics shaping the field: model validation, performance metrics, uncertainty quantification, algorithmic diversity, software implementation, and modeling protocols. The paper highlights persistent challenges around robust evaluation and reproducibility in geospatial ML workflows.
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🌍 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/
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🌍 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/
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🚨 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/ -
🚨 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.
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🚀 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.
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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/
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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/
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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.
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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.
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🚀 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
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🚀 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
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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
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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
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🛰️ 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/ -
🛰️ 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" -
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"