期刊
REMOTE SENSING IN ECOLOGY AND CONSERVATION
卷 6, 期 4, 页码 431-440出版社
WILEY
DOI: 10.1002/rse2.134
关键词
convolutional neural network; deep learning; drones; habitat classification; oyster reefs; semantic segmentation
资金
- North Carolina Division of Marine Fisheries (Marine Resources Fund) [2017-H-068]
- North Carolina Sea Grant/Space Grant Fellowship program [2017-R/MG-1710]
- Duke Bass Connections program
- Microsoft AI for Earth program
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据