SediNet: A configurable deep learning model for mixed qualitative and quantitative optical granulometry

Abstract

I describe a configurable machine-learning framework to estimate a suite of continuous and categorical sedimentological properties from photographic imagery of sediment, and to exemplify how machine learning can be a powerful and flexible tool for automated quantitative and qualitative measurements from remotely sensed imagery. The model is tested on a large dataset consisting of 400 images and associated detailed label data. The data are from a much wider sedimentological spectrum than previous optical granulometry studies, consisting of both well- and poorly sorted sediment, terrigenous, carbonate, and volcaniclastic sands and gravels and their mixtures, and grain sizes spanning over two orders of magnitude. I demonstrate the model framework by configuring it in several ways, to estimate two categories (describing grain shape and population, respectively) and nine numeric grain-size percentiles in pixels from a single input image. Grain size is then recovered using the physical size of a pixel. Finally, I demonstrate that the model can be configured and trained to estimate equivalent sieve diameters directly from image features, without the need for area-to-mass conversion formulas and without even knowing the scale of one pixel. Thus, it is the only optical granulometry method proposed to date that does not necessarily require image scaling. The flexibility of the model framework should facilitate numerous application in the spatio-temporal monitoring of the grain size distribution, shape, mineralogy and other quantities of interest, of sedimentary deposits as they evolve as well as other texture-based proxies extracted from remotely sensed imagery.

Technical feasibility of creating a beach grain size database with citizen scientists

“…The current model estimates average beach d50 to within 20% at five out of the seven sites. This error range is similar to the results of the wide sedimentological spectrum of well and poorly sorted sediment tested in Buscombe (2020).…”

Quantifying Below-Water Fluvial Geomorphic Change: The Implications of Refraction Correction, Water Surface Elevations, and Spatially Variable Error

“…Acquisition of such large datasets at scales comparable to high resolution RPAS surveys will require the use of terrestrial or airborne laser scanning. We anticipate that a more advanced ML approach, of which we are already seeing more widespread application within geomorphological fields (e.g., [75]), in combination with a significantly larger training dataset, will facilitate the development of a much improved AI model for predicting spatially variable elevation error for any given topographic dataset derived from the RPAS-SfM workflow.…”