Semi-Supervised Learning for Grain Size Distribution Interpolation

Abstract

High-resolution grain size distribution maps for geographical regions are used to model soil-hydrological processes that can be used in climate models. However, measurements are expensive or impossible, which is why interpolation methods are used to fill the gaps between known samples. Common interpolation methods can handle such tasks with few data points since they make strong modeling assumptions regarding soil properties and environmental factors. Neural networks potentially achieve better results as they do not rely on these assumptions and approximate non-linear relationships from data. However, their performance is often severely limited for tasks like grain size distribution interpolation due to their requirement for many training examples. Semi-supervised learning may improve their performance on this task by taking widely available unlabeled auxiliary data (e.g. altitude) into account. We propose a novel semi-supervised training strategy for spatial interpolation tasks that pre-trains a neural network on weak labels obtained by methods with stronger assumptions and then fine-tunes the network on the small labeled dataset. In our research area, our proposed strategy improves the performance of a supervised neural network and outperforms other commonly used interpolation methods.