/awesome-coastal

A curated list of awesome resources for coastal engineers and scientists

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This is a list of resources which coastal engineers and scientists might find useful. Since we're a fairly niche discipline, it's often difficult to find out about all the great things out in the community! This list brings together useful data-sets, tools and scripts that people might not have been aware of.

Contents


Contributing

If you have any resources that could be useful for coastal engineers and scientists please let us know! This list is mainly focussed on data-sets and software packages that have been developed (and less so on listing papers, reports or books). Contributing can be done in one of two ways:

  1. If you're comforatable with Github, make a pull request
  2. Or else, open a new issue
  3. You pull request or issue should provide a link to a resource and a description of what it does and why it might be useful.

By topic

Storm impacts (beach erosion, dune erosion etc.)

Wave modelling

  • 🆕 teslakit/teslakit: Python3 collection of libraries for numerical and statistical calculations and methodologies for handling global climate data.
  • 🆕 tsutterley/pyTMD: Python-based tidal prediction software that reads OTIS, GOT and FES formatted tidal solutions.
  • 🆕 CHLNDDEV/oceanmesh: Automatic coastal ocean mesh generation in Python and C++.
  • 🆕 akarimp/oceanlyz: Python toolbox for analysing wave time series data.
  • 🆕 anellenson/DecisionTree_WaveForecasts: Machine learning algorithm to determine and describe error patterns within wave model output.
  • chrisleaman/py-wave-runup: Python package with many empirical wave runup equations for sandy beaches.
  • TomasBeuzen/BeuzenEtAl_GP_Paper: Python Juypter notebook demonstrating a Gauassian-Process machine learning implementation of wave runup prediction.
  • Power et al. (2018) runup data: 1391 wave runup records collated from a variety of sources.
  • Field Research Facility LiDAR Runup measurements: Wave runup measurements taken by the LiDAR at the FRF in North Carolina, USA.
  • SWASH: SWASH is a general-purpose numerical tool for simulating unsteady, non-hydrostatic, free-surface, rotational flow and transport phenomena in coastal waters as driven by waves, tides, buoyancy and wind forces.
  • umwm/umwm: Opensource code University of Miami Wave Model, a third-generation spectral ocean wave model.
  • 🆕 georgebv/coastlib: coastlib is a Python library dedicated to solving problems related to the discipline of coastal engineering, such as enivronmental data collection (NOAA CO-OPS and NCEI, and WAVEWATCH III, etc.), extreme value analysis (EVA), data analysis and visualization, wave theories, and many more.

Terrestial and UAV video analysis

  • 🆕 mikeyt120/coastal-glare-estimation: A globally verified, Python tool to estimate when coastal observations are affected by glare.
  • 🆕 caiostringar/picoastal: Coastal video monitoring using a Raspberry Pi.
  • 🆕 caiostringar/deepwaves: Identifying wave breaking in videos.
  • Coastal Imaging Research Network: The CIRN organisation provides toolboxes to estimate bathymetry from video (cBathy) taken from fixed ARGUS stations or UAVs. Repos include the cBathy toolbox, UAV processing toolbox and image calibration and rectification routines. Currently all code uses MATLAB.
  • 🆕 mailemccann/coastalimagelib: Python-based library that produces common coastal image products intended for quantitative analysis of coastal environments.
  • CoastSnap: MATLAB toolbox to extract shorelines from pictures of beaches taken by the global community. More info here and here.
  • caiostringari/pywavelearn: pywavelearn provides a number of python scripts to process video of the swash zone. It provides functions to calibrate and rectify camera images, generate timestacks and classify pixels to track wave breaking.
  • simmonsja/cnn-shoreline-detect: Shoreline detection on oblique images of beaches using a HED CNN approach and Python.
  • dbuscombe-usgs/IR_waveclass: Software and data for training deep convolutional neural network models to classify wave breaker type from IR images of breaking waves in the surf zone using Python.
  • rgerum/cameratransform: Python package to fit camera properties and perform transformation from pixel to real-world coordinates and back again.
  • conlin-matt/SurfRCaT: Surf-Camera remote calibration tool that allows for the rectification of imagery from any coastal camera that views structures identifiable in lidar data.
  • jonghyunharrylee/pyPCGA: Example of bathymetric invesion using STWAVE.

Satellite remote sensing

  • 🆕SatelliteShorelines/CoastSeg: An interactive toolbox for downloading satellite imagery, applying image segmentation models, mapping shoreline positions and more.
  • 🆕mcuttler/CoastSat.islands: An extension of CoastSat adapted to measure the planform area of islands.
  • 🆕Sentinel-2 Water Edges Dataset: A dataset for deep learning applications containing annotated Sentinel-2 imagery.
  • kvos/CoastSat: CoastSat is a python package which processes LandSat imagery (over 30 years available) and extracts shorelines using a sub-pixel resolution technique.
  • dbuscombe-usgs/EarthAnnotator: A tool for web-based image annotation and efficient labeling pixels in images using Python
  • 🆕 Subpixel_waterlines: Python code and Jupyter Notebooks for extracting sub-pixel resolution waterlines from large multidimensional satellite datasets (e.g. from Open Data Cube or Google Earth Engine)

Early warning systems

Sediment size

  • 🆕 SandSnap: A collaborative project engaging citizen scientists in creating a sand grain database and educating the next generation about coastal processes.
  • 🆕 UNCG-DAISY/Instagrain: A camera for measuring sediment grain sizes with edge ML.

By location

Global

  • 🆕 Coastal Futures: A one stop viewer for 21st century projections of climatic impact-drivers (CIDs) leading to coastal impacts and risk
  • 🆕 Blue Earth Data: Global water data platform providing water level, wind, wave forecasts by Deltares.
  • 🆕 GeoMapApp: Downloadable application for browsing, visualizing and analyzing a diverse suite of curated global and regional geoscience data sets.
  • 🆕 Global Multi-Resolution Topography Data Synthesis: GMRT is a single, continuously ipdate compilation of global topobathy data worldwide.
  • 🆕 Opentopodata.org: Access a variety of open topo and bathy data from a REST API server.
  • 🆕 Awesome-DEM: List of global and local DEM resources (both free and proprietary).
  • Aviso FES2014 Global Tide Database: Predicted tide elevations, currents and loadings on a 1/16 x 1/16 degree grid for anywhere in the world. Simple to use python interface provided at cnes_aviso/fes.
  • WAVEWATCH III: Global wave model supplying hindcasts and forecasts around the world. Development has been recentl move to the open-source NOAA-EMC/WW3 repo.
  • fitnr/buoyant: Buoyant is a Python wrapper for grabbing buoy data from the National Buoy Data Center. It parses CSV from the SDF endpoint and images from the BuoyCam service.
  • ECMWF ERA5: Global climiate reanalysis which includes ocean waves. ERA5 data released so far covers the period from 1979 to 2-3 months before the present.
  • Global Sea Level Observing System: Tide observations from a global network of gauges.
  • Global Extreme Sea Level Analysis: Worldwide dataset of frequency and magnitude of extreme sea levels.
  • GEBCO global bathymetry: Global terrain model for ocean and land at 15 arc-second intervals.
  • OpenCoastS: On-demand ocean forecasting system for coastal areas providing water levels, velocities and wave parameters.
  • MERIT DEM: Improved global DEM at 3 arc-second resolution.
  • CoastalDEM: Global DEM with improved error correction around coastal areas.

Australia

New Zealand

USA

Europe

Community

  • Coastal List: Possibly the largest coastal engineer/scientist mailing list with over 6800 subscribers. Run by the University of Delware, job ads, student opportunities, workshops, training courses and conferences are all posted here.
  • Earth science jobs list: Google spreadsheet of list of available earth science faculty jobs.

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