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The Blue Research and Innovation Days Hackathon on Blue Economy with modern technological solutions

All blue economy (marine, fisheries, underwater) enthusiasts: data scientists and practitioners, students, researchers, representatives from both private and public sector, that work to improve the fisheries, marine, underwater and environmental management with applications in energy, geohazards management, underwater archaeology, or develop ICT solutions for marine, underwater monitoring are invited to join the hackathon co-organised by the NEANIAS, I4Sea and INFORE projects from April 21 to 23, 2021, online.

The event is kindly supported by ATHENA RC


During the 2-day hackathon the participants will brainstorm (compete, work) on the following four challenges:

The main challenge of this hackathon is to fuel new thinking in the holistic analysis of satellite, bathymetric, AIS, environmental, meteorological, fisheries and archaeological data and the interactions between fisheries, and the marine traffic. Copernicus, EMODnet, NEANIAS, i4sea, INFORE, FAO, Eurostat, Natura2000, AIS data from Marine Traffic are among the relevant datasets that will be made available.

The expected outcome will include a vision to cope with a wide range of topics in the Blue Economy area such as:

The event is of particular interest for students, master students, PhD students, post docs, early career researchers, SMEs, scientists and experts from start-ups.

Join this innovative tools oriented hackathon to apply your knowledge, challenge yourself in the hands-on competition and gain new skills! Prizes are also included!


Ask for more information Here

Hackathon Semantics

Title
Vessel navigation pattern detection
Category
Pattern Detection, Clustering, i4SEA
Short Description
Having a set of vessel trajectories, the challenge is to detect groups of vessels that exhibit
similarities in movement, in terms of spatio-temporal properties of the vessel’s location (e.g.,
weather, nearby fishing ports, nearby archaeological zones) and movement properties (e.g.,
speed, types of manoeuvring), i.e., similar navigation patterns (i4SEA).
(sub-)Tasks
  • Explore data that may be linked to mobility data
  • Explore candidate navigation patterns (e.g., origin, destination, trajectory shape, trip duration, change in speed)
  • Explore candidate clustering options (e.g., marine area, season)
  • Cluster mobility data
  • Report results
  • Repeat
Background
  • Classification of vessel activity in streaming data. DEBS 2020: 153-164
    • Ioannis Kontopoulos, Konstantinos Chatzikokolakis, Konstantinos Tserpes, Dimitris Zissis

  • Sea Area Monitoring and Analysis of Fishing Vessels Activity: The i4sea Big Data Platform,
    2020 21st IEEE International Conference on Mobile Data Management (MDM)
    • Panagiotis Tampakis, Eva Chondrodima, Aggelos Pikrakis, Yannis Theodoridis, Kostis Pristouris,
      Harry Nakos, Eleni Petra, Theodore Dalamagas, Andreas Kandiros, Georgios Markakis, Irida Maina,
      Stefanos Kavadas
Potential Considerations
Additional open spatial/temporal data sources available online may be used.
Indicative Data Sources
Recommended Tools
  • QGIS
  • Python
  • Pandas
  • scikit-learn

Title
Vessel future location prediction
Category
Location Prediction, Machine Learning, i4SEA
Short Description
Predictive mobility data analytics is critical for a wide range of marine applications. Future Location Prediction (FLP)
of vessels, i.e., the prediction of the anticipated location(s) of a vessel, taking into account the vessel's or
the population’s motion history, is of great importance to sea area monitoring.
This challenge involves the design and implementation of a solution to predict the future location of vessels.
Machine Learning solutions are common and may be considered. Mobility data may also be combined with additional data.
(sub-)Tasks
  • Process, explore, and analyze datasets
  • Choose approach
    • Machine Learning
      • Feature Engineering
      • Build training datasets
      • Run ML models
    • Non-ML
      • Design
      • Implement
      • Run
  • Report results
  • Repeat
Background
Future Location and Trajectory Prediction. 10.1007/978-3-030-45164-6_8:
Georgiou, Harris & Petrou, Petros & Tampakis, Panagiotis & Sideridis, Stylianos & Chondrodima, Eva & Pelekis, Nikos & Theodoridis, Yannis.
Potential Considerations
Additional open spatial/temporal data sources available online may be used.
Indicative Data Sources
Recommended Tools
  • QGIS
  • Python
  • Pandas
  • scikit-learn

Title
On-line Image Annotation Tool for Underwater Images
Category
User-Interface, Service-level, NEANIAS
Short Description
Build an online image annotation tool that allows end-users to provide point, polyline, and polygon annotations.
(sub-)Tasks

Build or customize an already available image annotation tool with the following functionalities

  • Let the user define a set of labels (e.g. seagrass, mud, rocky, gravel, etc.) to be annotated in the images
  • Let the user select the label whose instances are currently annotated
  • Let the user define a geometry (point, polyline or polygon) indicating the location of a specific annotation instance
  • Export the annotation data in a standardized format such as csv, json or yaml (multiple options if possible)
    indicating all the instances annotated and the associated geometry
  • The output format files must be properly documented for assuring interoperability
Background
Annotation tools are crucial parts of Machine Learning training and deployment pipelines as they allow
to easily and efficiently collect annotated data used in supervised-learning methods.
On-line annotation tools allow the collection and aggregation of annotated data in a simplified way from a potentially large number of users/annotators.
Potential Considerations
The tool must be implemented for on-line usage (e.g. as a micro-service).
Assessment of the developed tool's quality will be on the basis of the range if functionalities implemented, ease-of-use, practicality and efficiency.
Indicative Data Sources
  • Indicative underwater images
  • Images provided as demo and validation data for the NEANIAS uw-map and uw-mos services
    (participants may log-in using Gmail or Microsoft account). Please consult the documentation of the services for more details.
  • Any source of images found online or provided by the participants.
Indicative Recommended Tools

The participants may consider the following free, online, open-source image annotation tools:

Languages:

  • Python
  • Javascript
  • others

Libraries:

  • Scikit-Image
  • OpenCV
  • PILLOW
  • ImageMagick
  • Pandas
  • others

Papers:

  • Pizenberg, M., Carlier, A., Faure, E., & Charvillat, V. (2018). Web-based configurable image annotations.
    In Proceedings of the 26th ACM international conference on Multimedia (pp. 1368-1371). DOI
  • Dutta, A., & Zisserman, A. (2019). The VIA annotation software for images, audio and video.
    In Proceedings of the 27th ACM International Conference on Multimedia (pp. 2276-2279). DOI
  • Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008). LabelMe: a database and web-based tool for image annotation.
    International journal of computer vision, 77(1-3), 157-173. DOI

Title
Submarine cable route design and path planning
Category
Underwater Survey, Marine Construction, NEANIAS, INFORE
Short Description
Design the optimum route for a submarine cable considering all the obstacles (morphology,
ancient and modern shipwrecks and other artefacts, marine natural and cultural protected areas,
vessel activity) that may hinder the cable laying.
(sub-)Tasks

Scenario A: Process bathymetric data and identify potential risks for the cable route at Portsmouth NH, USA.

  1. Process the bathymetric data to produce the Digital Terrain Model (DTM) of the area using NEANIAS’s thematic service U1-BAT (http://bathyprocessing.dev.neanias.eu).
  2. Visualize the data
  3. Identify potential risks
  4. Design the cable route

Scenario B: Combine bathymetric data and Marine Vessel Traffic data for Aigina Island, Greece

  1. Download gridded bathymetric data from EMODnet.
  2. Create a polygon representing the submarine area of cultural interest.
  3. Process AIS data from MarineTraffic https://zenodo.org/record/3754481#.YGW04EgzZr0
  4. Visualise data producing a map consisting of different layers, using a layer per data source
  5. Design the candidate locations for cable routes
Background
Submarine cable systems carry almost 99% of internet traffic, with such great success, only 300 cable systems
can handle global internet usage. Submarine cables are expected to operate over 25 years after entering into service.
In order to construct a highly efficient submarine cable system to withstand for an extended period,
it is required to conduct a marine route survey to identify the seabed and substrate conditions and ascertain possible human activities.

Obstacles to undersea cables can be categorized as either natural threats (geological features like faults, landslides, channels etc)
or threats mainly caused by human activity (anchoring, dredging etc). Cultural heritage targets, such as ancient shipwrecks or other cultural artefacts,
should be also taken into account and should be avoided, and the relevant authorities should be notified (Ministries of Culture, relevant Departments of Underwater Antiquities, etc.)

Reading:

Potential Considerations

Scenario A:

  1. Calculate the slope of the DTM.
  2. Deliver a geodatabase retaining all the data/results of the marine survey or any other form of presentation.
Indicative Data Sources
Recommended Tools

Blue Research and Innovation Days Hackathon Programme committee:

  • Eleni Petra (NEANIAS)

  • Harry Nakos (I4Sea)

  • Antonis Deligiannakis (INFORE)

Responsibilities: Where, When, Organizational details, Media, Programme of Blue Week and Hackathon

Blue Research and Innovation Days Hackathon Scientific Committee:

  • E. Nomikou, WP2 Leader, Kalliopi Baika, Danai Lampridou, Valsamis -Makis Ntouskos (NEANIAS)

  • Theodore Dalamagas, Aggelos Pikrakis (i4SEA)

  • Antonis Deligiannakis, Κοnstantina Bereta (INFORE)

Responsibilities: Define problems – Challenges – Moto/Catchphrase – Participants Profile

1st Award: 3 day trip to Aegina for the whole winning team.

Guided tour to Athena RC, HCMR, other Research centers in ICT and Archeology. Mentoring support, invitations to all NEANIAS innovation and dissemination events

2nd Award: 1 laptop

Guided tour to ATHENA RC, HCMR, other Research centers in ICT and Archeology. Mentoring support, invitations to all NEANIAS innovation and dissemination events

3rd Award: 1 tablet

Guided tour to ATHENA RC, HCMR, other Research centers in ICT and Archeology. Mentoring support, invitations to all NEANIAS innovation and dissemination events