Comparison of three grain size measuring methods applied to coarse-grained gravel deposits

Measuring grain sizes in gravel‐bed rivers is crucial when studying river dynamics and sediment transport. Automated methodologies have been developed in recent years for detecting individual grains and measuring their size on digital imagery. These object‐based methodologies have mainly been applied to handheld imagery. Low‐cost and high‐resolution orthoimages covering long river reaches are nowadays accessible with the improvements in uncrewed aerial vehicles (UAV) and structure‐from‐motion (SfM) photogrammetry. Applying object‐based grain‐sizing methodologies to such orthoimages may provide wide‐scale information about the grain size spatial distribution along streambeds. We examined how accurate three object‐based models (BASEGRAIN, PebbleCountsAuto and GALET) were, by comparing their outcomes to in‐field manual measurements of grain sizes and manual grain labelling. We found that BASEGRAIN and PebbleCountsAuto underestimated grain sizes on average, whereas GALET generally overestimated grain size percentiles. Grain size measurements obtained by manually labelling grain features were consistent with in‐field measurements.

Section: Discussioncontrasting

confidence: 99%

Automated grain sizing from uncrewed aerial vehicles imagery of a gravel‐bed river: Benchmarking of three object‐based methods

Miazza,

Pascal,

Ancey

2024

“…We note here that for FH, we compared the predictions where grains are measured on undistorted images with a grid sampling approach. We do so because we expect a significant underestimation of the axes' lengths due to the occlusion of grains by the sandy matrix (for further details, see Garefalakis et al, 2023) segmentations yield more extensive and spatially resolved grain size information for fluvial settings (Section 4.3).…”

Section: Discussionmentioning

confidence: 99%

“…Such method‐specific traits become even more critical when analysing partially occluded or partially buried particles. This is the case for FH, where grain size measurements on images yielded different results than field‐based surveys where grains are manually measured (Garefalakis et al, 2023). Furthermore, our approach allows us to measure grains almost continuously and identify spatial variations within grain size data.…”

Section: Discussionmentioning

confidence: 99%

“…Set S1 contains 18 annotated tiles (14 for training and four as a test). The third dataset, referred to as FH (‘Finsterhennen’), consists of seven vertically orientated images taken with a handheld camera from fluvial sediment in the Finsterhennen gravel pit in Switzerland (Figure 1c; Garefalakis et al, 2023). These images differ from the APF and S1 sets in their orientation and depositional nature showing gravels embedded in a fine‐grained (<2 mm grain size) matrix.…”

Section: Methodsmentioning

confidence: 99%

“…(b) Uncrewed aerial vehicle (UAV) images from one site at the Swiss Sense River with homogenous image content and conditions are used in the specialized S1 dataset. (c) Images of a vertically oriented outcrop in the Finsterhennen gravel pit (Garefalakis et al, 2023) show gravel with fines and sand matrix between coarser grains. In all panels, the upper half shows the RGB image, whereas the lower half depicts the single‐channel greyscale image used for training and inference.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated detecting, segmenting and measuring of grains in images of fluvial sediments: The potential for large and precise data from specialist deep learning models and transfer learning

Mair,

Witz,

Do Prado

et al. 2023

Self Cite

The size of sedimentary particles in rivers bears information on the sediment entrainment or deposition mechanisms and the hydraulic conditions controlling them. However, collecting such data from coarse‐grained sediments is work intensive, both in the field and remotely. Therefore, attention has turned to machine learning models to improve the data acquisition. Despite their success, current methods need large quantities of data and yield results limited to a few percentile values of grain size datasets, often additionally affected by a systematic bias. In most cases, the root of these limitations is the challenge of accurately segmenting grains. Here, we present a new approach to improve the segmentation of individual grains based on the capacity of transfer learning in convolutional neural networks. Specifically, we re‐train a state‐of‐the‐art model for cell segmentation in biomedical images to find and segment coarse‐grained particles in images of fluvial sediments. Our results show that the performance in the segmentation tasks can be directly transferred to images of fluvial sediments and that our re‐trained models outperform existing methods. We document that our results are achievable with only 10%–20% of the data needed for training other deep learning models designed to measure the size of fluvial sediments. Moreover, we find that traits in our data control the segmentation performance. This enables data‐driven approaches to create specialist segmentation models. Additionally, comparing our automatically obtained datasets with the results retrieved from image and field‐based surveys confirms that improvements in segmentation are directly leading to more precise and more accurate grain size data even if data collection occurs in images taken at different conditions. Finally, we release a software package, the trained models and our data. The goal is to offer a tool to efficiently segment and measure grains in sediment images in an automated way, which can be adapted to different settings.

Pro+: Automated protrusion and critical shear stress estimates from 3D point clouds of gravel beds

Yager,

Shim,

Hodge

et al. 2024

The dimensionless critical shear stress (τ*c) needed for the onset of sediment motion is important for a range of studies from river restoration projects to landscape evolution calculations. Many studies simply assume a τ*c value within the large range of scatter observed in gravel‐bedded rivers because direct field estimates are difficult to obtain. Informed choices of reach‐scale τ*c values could instead be obtained from force balance calculations that include particle‐scale bed structure and flow conditions. Particle‐scale bed structure is also difficult to measure, precluding wide adoption of such force‐balance τ*c values. Recent studies have demonstrated that bed grain size distributions (GSD) can be determined from detailed point clouds (e.g. using G3Point open‐source software). We build on these point cloud methods to introduce Pro+, software that estimates particle‐scale protrusion distributions and τ*c for each grain size and for the entire bed using a force‐balance model. We validated G3Point and Pro+ using two laboratory flume experiments with different grain size distributions and bed topographies. Commonly used definitions of protrusion may not produce representative τ*c distributions, and Pro+ includes new protrusion definitions to better include flow and bed structure influences on particle mobility. The combined G3Point/Pro+ provided accurate grain size, protrusion and τ*c distributions with simple GSD calibration. The largest source of error in protrusion and τ*c distributions were from incorrect grain boundaries and grain locations in G3Point, and calibration of grain software beyond comparing GSD is likely needed. Pro+ can be coupled with grain identifying software and relatively easily obtainable data to provide informed estimates of τ*c. These could replace arbitrary choices of τ*c and potentially improve channel stability and sediment transport estimates.