Pointers to large-scale underwater datasets and relevant resources.

Modelling marine traffic in the ice-covered Baltic Sea using AIS data

Standardizing Marine Biological Data Working Group - An open community to facilitate the mobilization of biological data to OBIS.

Reverse-engineered interface to SonarPhone fish finders

This tool allows the identification and tracking of marine vessels by geolocation, MMSI, or via MarineTraffic URLs. It ensures user privacy via proxy support, and includes fallback strategies for optimal performance.

Juptyer notebook tutorials on using the data in the AWS pacific-sound registry for ocean soundscape research, education, and the arts

OBIS Python client

NASA Project; Plastic Marine Debris Classification-Machine Learning Software

Yi-Min Chou, Chien-Hung Chen, Keng-Hao Liu, and Chu-Song Chen, "Changing Background to Foreground: An Augmentation Method Based on Conditional Generative Network for Stingray Detection," IEEE International Conference on Image Processing, ICIP 2018, October 2018.

🐬 Code accompanying the article "Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images"

Access EMODnet Web Feature Service data through R

A global bioacoustics map featuring birds and marina megafauna

Hack for the Sea 2020 - October 2020 in Gloucester, MA

Marine Debris Detection with Commercial Satellite Imagery and Deep Learning.

Access EMODnet Web Coverage Service data through R

Scripts used in paper titled, 'Multi-year Seasonal Trends in Sea Ice, Chlorophyll Concentration, and Marine Aerosol Optical Depth in the Bellingshausen Sea.' These scripts are authored by S. Dasarathy.

A python library to filter AIS vessel data and do investigation.

A set of tools to visualise and analyse data collected by Plastic@Bay

Visualise, manipulate and access raster data from the north-west European Shelf sedimentary environment.

MariDataHarvest is a tool for scrapping and harvesting Automatic Identification System (AIS) data provided by marinecadastre then appending it with the weather and environment conditions provided by CMEMS and RDA at each geographical and UTC timestamp point.