Assessing marine litter in a highly polluted area in the Mediterranean: A multi-perspective approach in the Saronikos Gulf, Greece.

Marine litter poses a significant environmental challenge in the Saronikos Gulf, Greece. Employing an integrated approach, data from both beach and underwater sites were analyzed. The average litter density on beaches was 2.

Using Distributed Temperature Sensing for Long-Term Monitoring of Pockmark Activity in the Gulf of Patras (Greece): Data Processing Hints and Preliminary Findings.

Patras Gulf pockmark field (Western Greece) is a tectonically controlled field that has been activated at least twice by strong earthquakes (M5.4, 14 July 1993 and M6.4, 8 June 2008), and episodic gas seepages have been recorded in the past using geophysical means.

COVID-19-related litter pollution on Greek beaches and nearshore shallow water environments.

COVID-19 pandemic has led to an increase in certain types of litter, many of which are expected to end up in the marine environment. The present study aimed to monitor the pandemic-related litter pollution along the Greek coastal environment. Overall, 59 beach and 83 underwater clean-ups were conducted.

Insights into seafloor litter spatiotemporal dynamics in urbanized shallow Mediterranean bays. An optimized monitoring protocol using towed underwater cameras.

Monitoring of marine litter at the sea surface, the beaches and the seafloor is essential to understanding their sources, pathways and sinks and design effective clean-up programs or increase public awareness for reducing litter waste. Up until today, seafloor litter is the least exploited component of marine litter. Although the protocols for recording and assessing seafloor litter in the deep-sea environments are currently being actively defined and practiced, shallow seafloor litter survey protocols are still notably under-developed.

Automatic detection of seafloor marine litter using towed camera images and deep learning.

Aerial and underwater imaging is being widely used for monitoring litter objects found at the sea surface, beaches and seafloor. However, litter monitoring requires a considerable amount of human effort, indicating the need for automatic and cost-effective approaches. Here we present an object detection approach that automatically detects seafloor marine litter in a real-world environment using a Region-based Convolution Neural Network.