Publications

Spatial interpolation under incomplete covariate data set

Published in 17th International Conference on Scalable Uncertainty Management (SUM2026), 2026

New interval-based method for uncertainty propagation within a spatial interpolation process, when the data is possibly incomplete.

Recommended citation: Priscillia Labourg, Romain Guillaume, Jeremy Rohmer, and Sebastien Destercke (2026). Spatial interpolation under incomplete covariate data set. 17th International Conference on Scalable Uncertainty Management (SUM2026)

Which modelling choices shape your interpolation map? An approach based on optimal transport sensitivity analysis

Published in Preprint, 2025

Model-agnostic workflow to quantify the contributions of the modelling choices to spatial predictions

Recommended citation: Corinna Perchtold, Jeremy Rohmer, Augustin Thomas, Julie Lions. Which modelling choices shape your interpolation map? An approach based on optimal transport sensitivity analysis. Geoscientific Model Development. Submitted

Bayesian spatially varying coefficients modeling

Published in Spatial Statistics, 2025

Efficient bayesian spatially varying coefficients modeling for censored data using the vecchia approximation

Recommended citation: Idir Mohamed Yacine, Romary Thomas. Efficient bayesian spatially varying coefficients modeling for censored data using the vecchia approximation. Spatial Statistics. Spatial Statistics, 101032, doi:10.1016/j.spasta.2026.101032

Sensitivity Analysis

Published in Ecological Informatics, 2025

Importance ranking of data and model uncertainties in quantile regression forest-based spatial predictions when data are sparse, imprecise and clustered

Recommended citation: Rohmer, J. (2025). Importance ranking of data and model uncertainties in quantile regression forest-based spatial predictions when data are sparse, imprecise and clustered. Ecological Informatics. doi:10.1016/j.ecoinf.2025.103459 https://www.sciencedirect.com/science/article/pii/S1574954125004686

Geospatial Uncertainties with Intervals

Published in Lecture Notes in Networks and Systems, 2024

Recommended citation: Labourg, P., Destercke, S., Guillaume, R., Rohmer, J., Quost, B., Belbèze, S. (2024). Geospatial Uncertainties: A Focus on Intervals and Spatial Models Based on Inverse Distance Weighting. In: Lesot, MJ., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2024. Lecture Notes in Networks and Systems, vol 1174. Springer, Cham. https://doi.org/10.1007/978-3-031-74003-9_30 https://doi.org/10.1007/978-3-031-74003-9_30

group-SHAP uncertainty

Published in SOIL, 2024

Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach

Recommended citation: Rohmer, J., Belbeze, S., and Guyonnet, D.: Insights into the prediction uncertainty of machine-learning-based digital soil mapping through a local attribution approach, SOIL, 10, 679–697, https://doi.org/10.5194/soil-10-679-2024, 2024. https://soil.copernicus.org/articles/10/679/2024/

Literature review

Published in Journal of Geochemical Exploration, 2023

A literature review of methods used to establish urban soil geochemical background

Recommended citation: Belbèze, S., Rohmer, J., Négrel, P., Guyonnet, D. (2023). Defining urban soil geochemical backgrounds: A review for application to the French context; Journal of Geochemical Exploration. 254, 107298. https://www.sciencedirect.com/science/article/abs/pii/S0375674223001450