Learning in the Deep: Quantifying change in deep-sea benthic environments using 3D image reconstructions

已知海底热液喷口、暴露于地表的天然气水合物区和活珊瑚礁形成当地生物活动热点。这些生态系统中发生的复杂相互作用使得难以估计这些地区的生物量分布，这反过来又限制了我们预测人类活动在这些环境中的潜在影响的能力。虽然水下机器人技术的最新进展使人们有可能定期生成多公顷规模的高分辨率海底3D图像重建[1]，但大型底栖生物的密集种群（报告超过100份，000只来自不同物种的动物在一个领域[2]）使人工识别海底生物成为信息流的主要瓶颈，现场观测到科学解释所需的定量估计。该项目的目的是开发计算方法，以协助解释地理参考海底视觉图像，从而能够对巨型底栖生物分布的时间变化进行定量研究。这符合提高我们监测和了解海底生态系统对人类影响的反应的能力这一更广泛的范围。

Seafloor hydrothermal vents, surface-exposed gas hydrate fields and live coral reefs are known to form local hotspots for biological activity. The complex interactions that take place in these ecosystems makes it difficult to estimate the distribution of biomass in these areas, which in turn limits our ability to predict the potential effects of human activities in these environments. While recent advances in underwater robotics have made it possible to routinely generate multi-hectare scale, high-resolution 3D image reconstructions of the seafloor [1], the dense populations of mega-benthos (with reports of more than 100,000 animals from different species in a single field [2]) makes manual identification of seafloor organisms a major bottleneck in the flow of information from raw in-situ observation to the quantitative estimates needed for scientific interpretation. The aim of this project is to develop computational methods to assist the interpretation of georeferenced seafloor visual imagery, and so enable quantitative studies of the temporal changes the distribution of mega-benthic organisms. This fits within the broader scope of improving our ability to monitor and understand the response of seafloor ecosystems to human influence.