Benthic and substrate cover data derived from field photo-transect surveys for the Mackay to Capricorn management region of the Great Barrier Reef (GBR), May/June 2019

A subset of photoquadrats were uploaded to the CoralNet machine learning interface (https://coralnet.ucsd.edu/) and manually labelled for coral, algae or substrate type using 50 points per quadrat. Follow training of the machine, this training set enabled automatic annotation of all unclassified field images: the remaining field photos were uploaded to the database and 50 annotation points were overlaid on each of the images. Every point was assigned a benthic cover category from a label list automatically by the program. The resulting benthic cover data of each photo was linked to GPS coordinates, saved as an ArcMap point shapefile, and projected to Universal Transverse Mercator WGS-84.

将部分样方照片（photoquadrats）上传至CoralNet机器学习平台（https://coralnet.ucsd.edu/），并以每个样方50个采样点的规范，针对珊瑚、藻类或基质类型完成手动标注。完成模型训练后，该训练集可实现所有未分类野外图像的自动标注：将剩余野外照片上传至数据库，并在每张图像上叠加50个标注点。程序将自动从标签列表中为每个采样点分配底栖生物覆盖（benthic cover）类别。所得每张照片的底栖生物覆盖数据将与全球定位系统（GPS）坐标关联，保存为ArcMap点形状文件，并投影至通用横轴墨卡托WGS-84坐标系。