Author(s): M. Detert; V. Weitbrecht
Linked Author(s): Volker Weitbrecht, Martin Detert
Keywords: No Keywords
Abstract: Methods of image-based grain sieving are non-intrusive and low-cost, providing a more efficient way to obtain size distributions for non-cohesive fluvial bed material. This study presents inspirations how to analyze photos that have been taken under conditions that are suboptimal for image analysis and, therefore, suffer from diverse shortcomings. Examples of historical and extraterrestrial photos, photos taken with non-nadir view to the scenery, badly scaled photos, photos with limited view to the gravel bed, and further photos with out-of-the-box challenges are analyzed. Due to the broad variety of the examples given the potential of image-based sieving is shown – far beyond exclusively analyzing classical gravel beds for river science or engineering.
Year: 2020