The SandSnap Project: 2020 -- 2021 sieved grain-size data and associated sediment imagery

Overview

SandSnap is a collaborative project engaging citizen scientists to amass a sand beach grain size database and educating the next generation about coastal processes. See the following webpage for more details: https://sandsnap-erdcchl.hub.arcgis.com/  

SandSnap is funded by the US Army Corps of Engineers through the Coastal Inlets Research Program and the Regional Sediment Management Program.

SandSnap allows anyone with a cell phone to take an image of the sand with a US coin and measure the sand’s grain size using a deep learning neural network (Buscombe, 2020; McFall et. all, 2020). This model is trained using data obtained from sieved physical samples of sand. The purpose of this data release is to document the data sets that went into the SandSnap model, trained in Aug 2021, and used between August 2021 ongoing on this date October 26 2022.

Data formats and fields

usace_1024_aug_dry_set1_2_3_4_5_aug2021.csv

This is a spreadsheet that contains inputs for training the SandSnap SediNet model. SediNet is a deep-learning-based grain size predictor, by Dr Daniel Buscombe, Marda Science, LLC (https://github.com/DigitalGrainSize/SediNet). The SediNet model behind SandSnap v1 (August, 2021) is configured to estimate the grain size in pixels. A separate model is used to detect and size the coin, to estimate image scaling for grain size estimates in millimeters.

File: name of image

Latitude: WGS84 coordinate

Longitude: WGS84 coordinate

Population: an integer, identifying the site that the image came from. For internal model validation purposes (grouping error by site)

dry: 0= visibly wet sand, 1= visibly dry sand    

mm_px: millimeter per pixel scaling, computed from digitizing a coin in each image, as the diameter of the coin in millimeters, divided by the number of pixels across the diameter of the coin

d10: 10th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters

d16: 16th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters  

d25: 25th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters

d50: 50th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters    

d65: 65th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters    

d75: 75th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters    

d84: 84th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters    

d90: 90th percentile of the cumulative grain size distribution, obtained by sieve analysis, in millimeters    

mean: mean grain size, obtained by sieve analysis, in millimeters       

P10: 10th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

P16: 16th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

P25: 25th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

P50: 50th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

P65: 65th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

P75: 75th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

P84: 84th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

P90: 90th percentile of the cumulative grain size distribution, obtained by sieve analysis, in pixels

Pmean: mean grain size, obtained by sieve analysis, in pixels

GrainSizeAdditionalImagesTraining_Aug2021_latlong.xlsx assigns a coordinate to imagery and contains the following fields:

DatabaseObjectID
ATT ID
Name
Coin
mean
Latitude
Longitude

*.zip format files

Zipped folders contain original images, as well as augmented and tiled images for analysis. Tiled images are patches of original images with no coin scale. Patches are 1024 x 1024 x 3 pixels. Augmented images are tiles that have been flipped in both horizontal dimensions.

*.py format files

Python code for creating tiled and augmented images

References

Buscombe, D., 2020. SediNet: A configurable deep learning model for mixed qualitative and quantitative optical granulometry. Earth Surface Processes and Landforms, 45(3), pp.638-651.

McFall, B.C., Young, D.L., Fall, K.A., Krafft, D.R., Whitmeyer, S.J., Melendez, A.E. and Buscombe, D., 2020. Technical Feasibility of Creating a Beach Grain Size Database with Citizen Scientists. ERDC Coastal and Hydraulics Laboratory.

数据集概览

SandSnap是一项协作式研究项目，旨在动员公民科学家构建沙滩沙粒粒径数据库，并向年轻一代普及海岸过程相关知识。欲了解更多详情，请访问以下网页：https://sandsnap-erdcchl.hub.arcgis.com/

本项目由美国陆军工程兵团（US Army Corps of Engineers）通过海岸航道研究项目（Coastal Inlets Research Program）与区域沉积物管理项目（Regional Sediment Management Program）资助。

用户可通过智能手机拍摄带有美国硬币的沙滩沙粒照片，并借助深度学习神经网络测算沙粒粒径（参考文献：Buscombe, 2020; McFall et al., 2020）。该模型以经过筛分处理的实体沙样数据作为训练集。本次数据发布旨在记录用于训练SandSnap模型的数据集，该模型于2021年8月完成训练，并在2021年8月至2022年10月26日期间持续投入使用。

数据格式与字段说明

usace_1024_aug_dry_set1_2_3_4_5_aug2021.csv

该电子表格包含SandSnap的SediNet模型训练所需的输入数据。SediNet是由Marda Science有限责任公司（Marda Science, LLC）的Daniel Buscombe博士开发的基于深度学习的粒径预测模型（项目主页：https://github.com/DigitalGrainSize/SediNet）。SandSnap v1版本（2021年8月）所依托的SediNet模型，被配置为以像素为单位估算沙粒粒径；另有一款独立模型用于识别硬币并测算其尺寸，以此估算图像的缩放比例，进而将粒径结果转换为毫米单位。

各字段说明如下：
- 文件：图像文件名
- 纬度：WGS84坐标系下的纬度值
- 经度：WGS84坐标系下的经度值
- 采样点编号：整数类型，用于标识图像的采集站点，用于模型内部验证（按站点分组统计误差）
- 干湿状态：0表示肉眼可见的湿沙，1表示肉眼可见的干沙
- 毫米-像素缩放比：通过对每张图像中的硬币进行数字化测算得到，计算公式为硬币的实际直径（毫米）除以其在图像中的像素直径
- d10：经筛析法测得的累计粒径分布的10%分位值，单位为毫米
- d16：经筛析法测得的累计粒径分布的16%分位值，单位为毫米
- d25：经筛析法测得的累计粒径分布的25%分位值，单位为毫米
- d50：经筛析法测得的累计粒径分布的50%分位值，单位为毫米
- d65：经筛析法测得的累计粒径分布的65%分位值，单位为毫米
- d75：经筛析法测得的累计粒径分布的75%分位值，单位为毫米
- d84：经筛析法测得的累计粒径分布的84%分位值，单位为毫米
- d90：经筛析法测得的累计粒径分布的90%分位值，单位为毫米
- 平均粒径：经筛析法测得的沙粒平均粒径，单位为毫米
- P10：经筛析法测得的累计粒径分布的10%分位值，单位为像素
- P16：经筛析法测得的累计粒径分布的16%分位值，单位为像素
- P25：经筛析法测得的累计粒径分布的25%分位值，单位为像素
- P50：经筛析法测得的累计粒径分布的50%分位值，单位为像素
- P65：经筛析法测得的累计粒径分布的65%分位值，单位为像素
- P75：经筛析法测得的累计粒径分布的75%分位值，单位为像素
- P84：经筛析法测得的累计粒径分布的84%分位值，单位为像素
- P90：经筛析法测得的累计粒径分布的90%分位值，单位为像素
- P平均粒径：经筛析法测得的沙粒平均粒径，单位为像素

GrainSizeAdditionalImagesTraining_Aug2021_latlong.xlsx 用于为图像分配坐标，其包含以下字段：DatabaseObjectID、ATT ID、Name、Coin、mean、Latitude、Longitude。

*.zip 格式文件
压缩文件夹包含原始图像，以及用于分析的增强处理图像与裁切图像。裁切图像为不含硬币标尺的原始图像补丁，尺寸为1024 × 1024 × 3像素。增强处理图像为经水平翻转的图像补丁。

*.py 格式文件
用于生成裁切图像与增强处理图像的Python代码。

参考文献
Buscombe, D., 2020. SediNet: A configurable deep learning model for mixed qualitative and quantitative optical granulometry. Earth Surface Processes and Landforms, 45(3), pp.638-651.
McFall, B.C., Young, D.L., Fall, K.A., Krafft, D.R., Whitmeyer, S.J., Melendez, A.E. and Buscombe, D., 2020. 《利用公民科学家构建沙滩粒径数据库的技术可行性》. ERDC沿海与水力实验室（ERDC Coastal and Hydraulics Laboratory）