Published 2025-06-19

Convolutional neural networks for image-based sediment detection applied to a large terrestrial and airborne dataset

Data source language:

  • English

Data reference:

  • Chen Xingyu,Hassan Marwan A. & Fu Xudong.(2022).Convolutional neural networks for image-based sediment detection applied to a large terrestrial and airborne dataset.Earth Surface Dynamics,10(2),349-366.

Contributors of raw data:

  • Chen Xingyu / Tsinghua University

Data description:

  • The datasets (84 flume, 118 field photos) cover a wide range of fluvial environments and include a variety of field site and flume experiment images. The datasets were grouped into three subsets according to sediment and channel conditions: (1) flume channel (84 photos; photo size ~ 0.2 m × 0.2m); (2) forested mountain rivers (70 photos; photo size ~ 1m × 1m); and (3) sparsely vegetated large rivers (6 photos; photo size ~ 20m × 20m). To train the machine learning model to better distinguish sediments from field environmental elements (e.g., cohesive sands, wood, vegetation and water) and improve the model robustness, the datasets specifically collected 42 field photos primarily consisting of various environmental elements with limited sediment grains in the images.

Keywords and subjects:

  • Field images, Sediment identification

Availability:

  • publicly accessible
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