Alexander Kmoch
Alex is an Associate Professor in Geoinformatics and a Distributed Spatial Systems Researcher with many years of experience in geospatial data management and web- and cloud-based geoprocessing with a particular focus on land use, soils, hydrology, hydrogeology and water quality data. His interests include Discrete Global Grid Systems (DGGS), OGC standards and web-services for environmental and geo-scientific data sharing, modelling workflows and interactive geo-scientific visualisation. He is also the European co-chair of the OGC DGGS working group.
Session
Spatial machine learning has become increasingly crucial for environmental prediction tasks. Yet, current workflows in R and Python face challenges when scaling to high‑resolution, national‑level mapping and when integrating modern uncertainty‑aware methods. In this talk, I present a new Julia‑based spatial machine learning framework for digital soil mapping, focusing on national soil organic carbon (SOC) prediction in Estonia. The approach combines Random Forest models, stacked meta‑learning, and conformal prediction through the MLJ ecosystem, while developing an integration port to Julia of the IGEO7 discrete global grid system (DGGS) to impose a hierarchical spatial structure.
This approach targets persistent issues in spatial ML, such as autocorrelation, multi‑scale dependencies, and computational efficiency. It implements DGGS‑based multi‑resolution covariate aggregation, spatially aware cross‑validation, Shapley values, and area‑of‑applicability (AOA) assessment using the Dissimilarity Index method. Initial results demonstrate improved spatial fidelity, scalable high-resolution prediction, and more transparent communication of uncertainty.
This work showcases how Julia’s speed and composability enable a modern, reproducible, and scalable approach to spatial machine learning in comparison to what conventional Python/R workflows currently offer.