Deep learning for coastal resource conservation: automating detection of shellfish reefs
Deep learning for coastal resource conservation: automating detection of shellfish reefs
复制标题
沿海资源保护的深度学习：贝类珊瑚礁的自动检测
DOI：
10.1002/rse2.134
复制
发表时间：
2020
影响因子：
5.5
通讯作者：
D. Johnston
中科院分区：
文献类型：
--
作者：
J. Ridge;P. Gray;Anna E. Windle;D. Johnston
在人为和气候驱动的变化面前，了解沿海自然资源的范围和健康状况越来越重要。由于现有的遥感数据无法捕捉复杂的空间生境模式，因此难以对沿海生态系统进行有效监测。为了帮助管理人员和研究人员避免低效的传统制图工作，我们开发了一种深度学习工具（OysterNet），该工具使用无人机系统（UAS）图像来自动检测和描绘牡蛎礁，这是一种被证明难以远程监控的生态系统。OysterNet是一种卷积神经网络（CNN），用于评估潮间带牡蛎礁的范围，手动和自动划定之间的总面积差异仅为8%，部分原因是OysterNet能够检测在手动划界过程中被忽略的牡蛎。OysterNet的进一步培训可以评估牡蛎礁的高度和密度，并纳入更多的沿海生境类型。未来的迭代将应用于高分辨率卫星数据，以便在更大范围内进行有效管理。
It is increasingly important to understand the extent and health of coastal natural resources in the face of anthropogenic and climate‐driven changes. Coastal ecosystems are difficult to efficiently monitor due to the inability of existing remotely sensed data to capture complex spatial habitat patterns. To help managers and researchers avoid inefficient traditional mapping efforts, we developed a deep learning tool (OysterNet) that uses unoccupied aircraft systems (UAS) imagery to automatically detect and delineate oyster reefs, an ecosystem that has proven problematic to monitor remotely. OysterNet is a convolutional neural network (CNN) that assesses intertidal oyster reef extent, yielding a difference in total area between manual and automated delineations of just 8%, attributable in part to OysterNet's ability to detect oysters overlooked during manual demarcation. Further training of OysterNet could enable assessments of oyster reef heights and densities, and incorporation of more coastal habitat types. Future iterations will be applied to high‐resolution satellite data for effective management at larger scales.
影响因子：
14.6
作者：
Martijn Gough
通讯作者：
Martijn Gough
影响因子：
5.5
作者：
Chilson, Carmen;Avery, Katherine;Kelly, Jeffrey
通讯作者：
Kelly, Jeffrey