Automated Classification for Mapping submarine structures by Artificial Intelligence strategies

Project reference: 2111

Automatic techniques for recognition of submarine structures (e.g. canyons) are an absolute novelty in the Oceanographic field. Excluding some preliminary experiments (Ismail et al -2015, Huvenne -2002), right now the recognition on the seabed of structures like canyons, mud volcanoes and others  has been entrusted to the experience of scientists that manually highlight them on the  seabed images.

Considering the growth of available data, this approach cannot be followed anymore. Thanks to the huge amount of already analysed data a new approach based on AI techniques can be preferred.

During this two months internship the selected student will do some experiments to setup an automated methods to recognize these kinds of structures.

This is picture shows the Ionian Calabrian continental margin ( Ceramicola et al 2014). The colour layer (red to blue) is the morphobathymetric data acquired during the MAGIC project funded by the Department of Civil Protection to allow the realisation of the first maps of geohazards of the Italian continental margins. In legend are indicated the different geohazards features mapped by an interpreter.

Project Mentor: Silvia Ceramicola

Project Co-mentor: Veerle Huvenne

Site Co-ordinator: Massimiliano Guarrasi

Participants: Mario Udo Gaimann, Raska Soemantoro

Learning Outcomes:
The selected student will learn to manage oceanographic data to find the most important seabed structures.
Increase student’s skills about:
– PYTHON
– AI libs (e.g. PyTorch, Keras, TensorFlow, …)

Student Prerequisites (compulsory):
The student will need to have the following compulsory skills:

• Python
• Python main scientific libraries (at least basic knowledge):
  • Numpy
  • Pandas
  • Matplotlib
  • …
• At least one of the following packages:
  • PyTorch
  • Tensorflow, Keras

Student Prerequisites (desirable):
Experience in image recognition methods

Training Materials:
None

Workplan:

• Week 1: Training week with the other SoHPC students
• Week 2: Training by CINECA (HPC facility, slurm batch scheduler, VNC, Blender, …) and OGS staff (How to read the data, how to visualize and explore them)
• Week 3: Setup the workplan and start the work according with the student abilities and interest
• Week 3 (end): Workplan ready.
• Week 4-6: Continue the work on structures recognition
• Week 7-8: Prepare the final video and report
• Week 8 (end): The final video and report will be submitted

Final Product Description:
Our final result will be an automated tool to recognize seabed structures form oceanographic data. The selected student will also prepare a short video and a final report illustrating his/her work.

Adapting the Project: Increasing the Difficulty:
Depending on the student skills he/she can try to recognize many different type of structures.

Adapting the Project: Decreasing the Difficulty:
In the case the student will not be able to complete the desired tool, he/she will work on the visualization of the oceanographic data using the standard visualization tools

Resources:
The oceanographic data and the standard software to inspect them will be given by OGS. All the needed HPC software ( mainly Python, Pytorch, Keras, Fensorflow, Pandas, numpy) is released open source and already  available on the CINECA clusters that will be used by the students with their own provided accounts.

Organisation:
CINECA – Consorzio Interuniversitario