Earth system modelling (ESM) is essential for understanding past, present and future Earth
processes. Deep learning (DL), with the data-driven strength of neural networks, has …

Particle settling velocity is a fundamental parameter in sedimentology and engineering, and
has accordingly received much attention in the literature. Grain properties, such as shape …

It will be practically useful to know the mechanical properties of granular materials by only
taking a photo of particles. This study attempts to deal with this challenge by develo** a …

This article proposes a new methodological approach to measure and map the size of
coarse clasts on a land surface from photographs. This method is based on the use of the …

Satellite remote sensing is transforming coastal science from a “data-poor” field into a “data-
rich” field. Sandy beaches are dynamic landscapes that change in response to long-term …

Grain size analysis is the key to understand the sediment dynamics of river systems. We
propose GRAINet, a data-driven approach to analyze grain size distributions of entire gravel …

Abstract Recent advances in Artificial Intelligence (AI), particularly the rise of deep learning,
are revolutionizing data collection and analysis in many aspects of the Earth Sciences …

We develop a remote wave gauging technique to estimate wave height and period from
imagery of waves in the surf zone. In this proof-of-concept study, we apply the same …

Data on grain sizes of pebbles in gravel-bed rivers are of key importance for the
understanding of river systems. To gather these data efficiently, low-cost UAV (uncrewed …

Image-based grain sizing has been used to measure grain size more efficiently compared
with traditional methods (eg, sieving and Wolman pebble count). However, current methods …