The GeoHab Machine Learning Working Group (MLWG) is an association of researchers with interest in Machine Learning methods for benthic habitat mapping.

Purpose:

1. To promote machine learning education within the GeoHab community by sharing methods and expertise.
2. To work towards consensus on optimal modelling approaches for different benthic habitat mapping applications.
3. To establish datasets that are useful for benchmarking statistical modelling methods within the field.

MLWG organised the GeoHab 2025 MLWG competition, where participants were asked to predict the mean grain size of physical seafloor sediment samples on the Scotian Shelf (off Nova Scotia, Canada), using bathymetric, oceanographic, and geospatial variables. The dataset is still available for download on the competition website: https://www.kaggle.com/competitions/geohab-mlwg-competition-2025