Correction: Lopez-Vazquez et al. Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories. 2020, , 726.

The authors wish to correct the following error in the original paper [...

An Automated Pipeline for Image Processing and Data Treatment to Track Activity Rhythms of in Relation to Hydrographic Conditions.

Imaging technologies are being deployed on cabled observatory networks worldwide. They allow for the monitoring of the biological activity of deep-sea organisms on temporal scales that were never attained before. In this paper, we customized Convolutional Neural Network image processing to track behavioral activities in an iconic conservation deep-sea species-the bubblegum coral -in response to ambient oceanographic conditions at the Lofoten-Vesterålen observatory.

A Flexible Autonomous Robotic Observatory Infrastructure for Bentho-Pelagic Monitoring.

This paper presents the technological developments and the policy contexts for the project "Autonomous Robotic Sea-Floor Infrastructure for Bentho-Pelagic Monitoring" (ARIM). The development is based on the national experience with robotic component technologies that are combined and merged into a new product for autonomous and integrated ecological deep-sea monitoring. Traditional monitoring is often vessel-based and thus resource demanding.

Video Image Enhancement and Machine Learning Pipeline for Underwater Animal Detection and Classification at Cabled Observatories.

An understanding of marine ecosystems and their biodiversity is relevant to sustainable use of the goods and services they offer. Since marine areas host complex ecosystems, it is important to develop spatially widespread monitoring networks capable of providing large amounts of multiparametric information, encompassing both biotic and abiotic variables, and describing the ecological dynamics of the observed species. In this context, imaging devices are valuable tools that complement other biological and oceanographic monitoring devices.