Reproducible Jupyter notebooks, tutorials, and hands-on modules for ocean and coastal AI research
Learn to build and create pandas DataFrames for data analysis workflows in Python.
Perform common operations on pandas DataFrames: filtering, grouping, and transforming data.
Create charts and visualizations for exploring and presenting ocean and coastal data.
Analyze maritime traffic patterns and disruptions during natural hazards such as hurricanes using satellite data and AI workflows.
Learn to classify fish using AI and machine learning techniques for sustainable fisheries management.
Learn to model and analyze fish habitat suitability using AI and machine learning techniques for sustainable fisheries management.
Learn to build and train a CNN model for coral classification using AI and machine learning techniques for sustainable coral reef management.
This notebook demonstrates an end-to-end cyberinfrastructure (CI) workflow connecting coastal hazard signals (NOAA CO-OPS water-level anomalies and NOAA NCEI storm exposure features) with operational disruption metrics from AIS vessel data near ports. Learn how to build daily port × date panel datasets, identify extreme hazard events, compute resilience metrics (drop and recovery), and apply regression models to analyze impacts of hazards on vessel activity in Houston and Norfolk.
Use flux tower observations, satellite imagery, and weather data to map evapotranspiration in coastal wetlands and estuarine reserves at 30 m resolution.