📄 New our preprint: Moving Beyond Spatial and Random Cross-Validation in Environmental Modelling: A Call for Prediction-Domain Adaptive Evaluation arxiv.org/abs/2605.136... #SpatialStats #Ecology #MachineLearning
Who's Who of AI
Jakub Nowosad
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Researcher with public evidence across AI research.
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Computational geographer. Associate Professor at AMU, Poznan, Poland. Co-author of http://r.geocompx.org, http://py.geocompx.org, and http://tmap.geocompx.org books. #rstats #rspatial #geocompx
https://jakubnowosad.com/
What they're sharing
Moving beyond spatial and random cross-validation in environmental modelling: a call for prediction-domain adaptive evaluation arxiv.org
erdkunde.uni-bonn.de
Rethinking Validation for Spatial Machine Learning: Takeaways from the Talk – Thinking in spatial patterns jakubnowosad.com
Where your models can be trusted: evaluating spatial machine learning reliably jakubnowosad.com
Articles & links
🌍 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. www.erdkunde.uni-bonn.de/article…
Notes from ML4EO 2026 🌍 A short blog post summarizing my talk and workshop from the ML4EO conference on spatial machine learning validation. jakubnowosad.com/posts/2026-0... #rspatial #EarthObservation #MachineLearning #RemoteSensing #GeoSpatial
🚦 New workshop from #ML4EO2026: "Where your models can be trusted" Learn how to evaluate spatial ML using kNNDM CV, Area of Applicability, and more. All with R examples. Materials: jakubnowosad.com/ml4eo2026wor.... #SpatialML #MachineLearning #RStats #RSpatial
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