Coral reef research and management efforts can be improved when supported by reef maps providing local-scale details across global extents. However, such maps are difficult to generate due to the broad geographic range of coral reefs, the complexities of relating satellite imagery to geomorphic or ecological realities, and other challenges. However, reef extent maps are one of the most commonly used and most valuable data products from the perspective of reef scientists and managers. Here, we used convolutional neural networks to generate a globally consistent coral reef probability map—a probabilistic estimate of the geospatial extent of reef ecosystems—to facilitate scientific, conservation, and management efforts. We combined a global mosaic of high spatial resolution Planet Dove satellite imagery with regional Millennium Coral Reef Mapping Project reef extents to build training, validation, and application datasets. These datasets trained our reef extent prediction model, a neural network with a dense-unet architecture followed by a random forest classifier, which was used to produce a global coral reef probability map. Based on this probability map, we generated a global coral reef extent map from a 60% threshold of reef probability (reef: probability ≥ 60%, non-reef: probability < 60%). Our findings provide a proof-of-concept method for global reef extent estimates using a consistent and readily updateable methodology that leverages modern deep learning approaches to support downstream users. These maps are openly-available through the Allen Coral Atlas.

中文翻译：

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使用卷积神经网络生成的全球珊瑚礁概率图

在珊瑚礁地图的支持下，珊瑚礁研究和管理工作可以得到改善，该地图提供了全球范围内的局部尺度细节。然而，由于珊瑚礁的地理范围广泛、卫星图像与地貌或生态现实相关的复杂性以及其他挑战，此类地图难以生成。然而，从珊瑚礁科学家和管理者的角度来看，珊瑚礁范围图是最常用和最有价值的数据产品之一。在这里，我们使用卷积神经网络生成全球一致的珊瑚礁概率图——对珊瑚礁生态系统地理空间范围的概率估计——以促进科学、保护和管理工作。我们将高空间分辨率 Planet Dove 卫星图像的全球镶嵌与区域千年珊瑚礁测绘项目的珊瑚礁范围相结合，以构建培训、验证和应用数据集。这些数据集训练了我们的珊瑚礁范围预测模型，这是一个具有密集 Unet 架构的神经网络，后跟随机森林分类器，用于生成全球珊瑚礁概率图。基于此概率图，我们从珊瑚礁概率的 60% 阈值（珊瑚礁：概率 ≥ 60%，非珊瑚礁：概率 < 60%）生成了全球珊瑚礁范围图。我们的研究结果使用一致且易于更新的方法为全球珊瑚礁范围估计提供了一种概念验证方法，该方法利用现代深度学习方法来支持下游用户。