This paper describes benthic coral reef community composition point-based field data sets derived from georeferenced photoquadrats using machine learning. Annually over a 17 year period (2002–2018), data were collected using downward-looking photoquadrats that capture an approximately 1 m² footprint along 100 m–1500 m transect surveys distributed along the reef slope and across the reef flat of Heron Reef (28 km²), Southern Great Barrier Reef, Australia. Benthic community composition for the photoquadrats was automatically interpreted through deep learning, following initial manual calibration of the algorithm. The resulting data sets support understanding of coral reef biology, ecology, mapping and dynamics. Similar methods to derive the benthic data have been published for seagrass habitats, however here we have adapted the methods for application to coral reef habitats, with the integration of automatic photoquadrat analysis. The approach presented is globally applicable for various submerged and benthic community ecological applications, and provides the basis for further studies at this site, regional to global comparative studies, and for the design of similar monitoring programs elsewhere.

本文描述了底栖珊瑚礁群落组成基于点的野外数据集，这些数据集是使用机器学习从地理参照光四足动物得出的。在过去的17年（2002-2018年）中，每年使用向下看的照片四边形收集数据，这些照片四边形沿沿礁岩坡度和整个鹭岛礁石平面分布的100 m-1500 m横断面调查捕获了约1 m ^2的足迹（ 28公里²），澳大利亚大堡礁南部。在对算法进行初始手动校准之后，通过深度学习自动解释了光敏四足动物的底栖动物群落组成。所得数据集支持对珊瑚礁生物学，生态学，制图和动力学的理解。已经发布了类似的方法来获取海草栖息地的底栖生物数据，但是在这里，我们结合了自动四象限分析，对适用于珊瑚礁栖息地的方法进行了调整。提出的方法在全球范围内适用于各种淹没和底栖生物的生态应用，并为该地点的进一步研究，区域至全球的比较研究以及在其他地方设计类似的监测计划提供了基础。