Environment

STeMP: Spatio-Temporal Modelling Protocol

Jan Linnenbrink, Jakub Nowosad, Marvin Ludwig, Anna Frederike Jablotschkin, Fabian Schumacher, Teja Kattenborn, Hanna Meyer

Featured August 13, 2026

Original Paper

AI-generated analysis — This is SciGrove's AI interpretation of the paper, not peer-reviewed content. Always refer to the original paper.

Simply

A new tool called STeMP helps scientists clearly describe their environmental computer models and even warns them about common mistakes, making their work more trustworthy and easier for others to understand and reuse.

In depth

The paper introduces STeMP, a Spatio-Temporal Modelling Protocol designed to standardize reporting and guide the development of machine-learning models in environmental and geoscientific applications. It addresses the current lack of transparency and reproducibility by providing a structured framework and an accompanying web application that not only automates parts of the reporting but also proactively warns users about common pitfalls specific to spatio-temporal data, such as issues with spatial autocorrelation and evaluation strategies.

Key Takeaways

1
Introduces STeMP, a standardized protocol for transparent reporting of spatio-temporal machine-learning models.
2
Provides an R-package with a web application that semi-automates protocol filling and generates warnings for common pitfalls.
3
Addresses critical issues like spatial autocorrelation and inappropriate cross-validation strategies in environmental modeling.

Conceptual Flow

HIGH LEVEL

Standardizing Model Reporting

This tool helps scientists write down all the important details about their computer models in a clear, organized way.

Model Data

Spatial Data

Model Decisions

Organize and Check

Clear Model Report

Potential Issues

Improving Model Trustworthiness

By making reports clear and pointing out problems, the tool helps everyone trust the model's predictions more.

Confusing Reports

Hidden Problems

Add Clarity and Warnings

Trustworthy Predictions

Reproducible Science

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