MOOC-AI: Week - 4 Mooc

Topic 4a - Introduction To Monitoring The Oceans

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  Whether it is following rising sea levels, mapping ocean surface winds and currents, monitoring water quality, or helping track sea turtles or jellyfish blooms, satellites have made a huge difference to our ability to monitor the world’s oceans. And because the ocean, land and atmosphere are inextricably linked, what happens in the oceans makes a big difference for life on Earth.
  
  Key ocean monitoring satellites are as follows:
  
  Copernicus Sentinel-1 satellites carry a synthetic aperture radar instrument (SAR) that provides radar data to monitor sea ice, marine wind, oil spills and marine navigation.
  
  Copernicus Sentinel-2 satellites collect high resolution ocean colour data in coastal areas.
  
  The Copernicus Sentinel-3 satellites collect observations of global ocean colour, sea surface temperature and sea surface height.
  
  Copernicus Sentinel-6 and Jason-3 satellites provide global sea surface height observations for climate monitoring and ocean and seasonal forecasts.
  
  In addition, EUMETSAT’s Meteosat satellites provide hourly measurements of sea surface temperature, while the Metop satellites provide global observations of ocean surface wind, sea ice, and sea surface temperature, plus collect in situ observations of the ocean through the ARGOS system.
  
  While satellites play a key role in providing the big picture of what is happening in the oceans it is also important to collect in situ data in the oceans to ground truth satellite measurements. Unfortunately, this is more difficult as they are so vast, but this is one area where AI can really fill in the gaps by predicting measurements where data are sparse. However, this can also pose problems, because we need the ground truth from the oceans in order to train the models in the first place. This is why in some cases, AI is used for data fusion between satellite data and in situ networks. AI and machine learning can also help to provide new insights on marine ecosystems, manage human activities on the oceans and monitor the impact of climate change.
  
  The Copernicus Marine Service (or Copernicus Marine Environment Monitoring Service) is the marine component of the Copernicus Programme of the European Union.
  
  CMEMS provides free, regular and systematic information on the state of the Blue (physical), White (sea ice) and Green (biogeochemical) ocean, on a global and regional scale. It is funded by the European Commission (EC) and implemented by Mercator Ocean International.
  
  It is designed to serve EU policies and international legal commitments related to ocean governance, to cater for the needs of society at large for global ocean knowledge and to boost the Blue Economy across all maritime sectors by providing free ocean data and information.
  
  CMEMS provides key inputs that contribute to combating pollution, marine protection, maritime safety and routing, sustainable use of ocean resources, developing renewable marine energy resources, supporting blue growth, climate monitoring, forecasting, and more.
  
  It also aims to increase awareness amongst the general public by providing European and global citizens with information about ocean-related issues.
Topic 4b – Tracking Ships

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  Monitoring the movement of ships and their activity at seaports, as well as current and forecasted weather conditions, is critical for the shipping industry, from trade to tourism.
  
  Deep Blue Globe is a start-up that came out of a Copernicus Hackathon at EUMETSAT in Darmstadt and then developed with the support of the ESA Business Incubation Centre in Hessen and Baden-Württemberg.
  
  They have developed an AI-based autonomous ship navigation system that considers real time maritime traffic and weather conditions based on satellite data for any vessels, in particular in the shipping industry.
  
  Deep Blue Globe uses data from the Copernicus Programme and also benefited from WEkEO as a data access and development platform.
  
  The company is using AI/machine learning to develop a number of different products and services, including:
  
  Poseidon - a maritime autonomous navigation system powered by artificial intelligence considering real time maritime traffic and weather conditions based on satellite data for any vessels, in particular in the shipping industry. The benefits of smart ship-routing include: reducing CO₂ and SOx emissions, fuel cost saving and an increase in safety.
  
  OneWave - a 24/7 service of Wave Front identification in the open sea that uses Copernicus satellite data (mostly altimetry) to complement the existing tsunami early warning systems.
  
  Jupyter Notebook Task: AI for ship classification
  
  For this Jupyter Notebook task you will complete ship classification using Sentinel-1 and OpenSAR ship datasets with a convolutional neural network (CNN) used for large-scale image recognition.
  
  Ship detection and classification from satellite imagery is useful for marine surveillance, so that governments can be aware of what type of ships there are and what they are doing - from fishing, drilling and exploration to cargo transport and tourism, or if they will have any impact on the security, safety, economy, or environment.
  
  You can view the video for this tutorial both here on FutureLearn as well as at the top of the Jupyter Notebook.
  
  For this Jupyter Notebook workflow tutorial please follow this link to go to the WEkEO Jupyter Hub or click on the link in the ‘See Also’ section below.
  
  If you have any questions, comments or would just like to discuss this Notebook with your fellow learners, please write in the discussion below.
Topic 4c - Marine Safety

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  Marine debris is any persistent solid material that is manufactured or processed and directly or indirectly, intentionally or unintentionally, disposed of or abandoned into the marine environment.
  
  Marine debris is a hazard to navigation safety, endangers wildlife and is a threat to human health.
  
  With optical satellite images and a machine learning approach, debris can be detected and characterised, and we can monitor their drift with ocean forecast products, which helps to make marine navigation more safe.
Topic 4d - Monitoring Marine Life: Part 1 - Fish

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  Machine learning is becoming increasingly important for monitoring marine life.
  
  Being able to monitor the distribution of key areas for fish in the ocean is very important to improve our understanding of marine ecosystems. Better knowledge means we can manage marine resources in a more sustainable way and also better understand the effects of climate change in the future.
  
  Fish are also a vital source of food for billions of people worldwide and they also provide a livelihood to millions. However, fish stocks are at threat due to overfishing and climate change.
  
  In this video, Georgios Sylaios explains how satellite data and ML can be used to investigate the distribution of pelagic fish in the Mediterranean Sea.
Topic 4d - Monitoring Marine Life: Part 2 - Jellyfish

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  Large swarms of jellyfish can cover hundreds of square miles of ocean and can cause both economic and environmental damage. Examples of this include reducing tourism due to the closing down of beaches and damage to fisheries due to the clogging of fishing nets and preying on eggs and larvae of fish.
  
  With satellite images, jellyfish swarms are detected with ML and we can monitor their drift with ocean forecast products.
  
  JellyX is an ocean-wide monitoring tool for the detection of jellyfish swarms and the prediction of their drift, based on ocean data. Fish farmers, whose activity is severely impacted by this phenomenon, can detect jellyfish swarms in advance, preventing important profit losses. With high estimation accuracy of jellyfish presence or absence, JellyX provides fish farmers with the solution for predicting and preventing the economic impacts of jellyfish outbreaks.
  
  JellyX combines:
  
  Copernicus Marine Service products
  
  Copernicus Sentinel 3 satellite
  
  In-situ observations and ancillary data sources
  
  automatic classification algorithms
  
  The Copernicus Marine Service products used in JellyX are currently focused on the Mediterranean Sea but concern satellite datasets and biogeochemical models that will be applied to other oceans and seas.
  
  Supervised machine learning techniques are used to estimate the abundance of jellyfish and classify the risk of proliferation as low, medium and high risk.
Topic 4d - Monitoring Marine Life: Part 3 - Turtles

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  All marine turtle species are experiencing serious threats to their survival. These threats include pollution, accidental killing in fishing nets or by fishing hooks, and changes to their habitats such as coral reefs, mangrove forests, seagrass beds, as well as their nesting beaches.
  
  In order to minimise the death of turtles from fishery equipment, it is important to know where the turtles are located, so that they can be avoided. Adult turtles can be easily equipped with beacons that allow tracking them by satellite and determining areas they occupy, but this is not the case for juveniles, who are generally too small to be equipped with the tracking beacons. So, in order to understand where juvenile turtles are and when (known as ‘lost years’), Philippe Gaspar has been using the Sea Turtle Active Movement Model (STAMM), which was designed by Philippe and Maxime Lalire at CLS (Collecte Localisation Satellites).
  
  STAMM makes extensive use of Copernicus Marine Service products, especially global current velocity and primary production, which is used to simulate where juvenile turtles may go by the currents and speed the turtle is swimming, and the most favorable habitats. By using AI, improvements can be made to STAMM, in order to generate more accurate models of turtle movements across the oceans.
Topic 4e - Expanding Our Vision Of The Sea Surface

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  Remote sensing data provides a huge number of sea surface observations, but it cannot give direct information on deeper ocean layers, which can only be provided by sparse in situ data. The combination of measurements collected by satellite and in situ sensors represents one of the most effective strategies to improve our knowledge of the interior structure of the ocean ecosystems.
  
  Neural networks allow us to expand this vision of the sea surface that we get from satellites and in situ observations. This means we can obtain 3D reconstructions of the ocean by using sea surface temperature (SST) and ocean colour surface fields with a multi-layer-perceptron (MLP) network.
  
  These two variables are of primary importance for many upper-ocean biophysical processes. Artificial neural networks can efficiently model eventual non-linear relationships among input variables, and the choice of the predictors is thus crucial to build an accurate model.
Topic 4f – Using ML To Differentiate Between Sediment and Chlorophyll

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  Being able to tell the difference between chlorophyll and sediment is fundamentally important for ocean researchers using EO data. It is very difficult to be able to tell the difference between sediment and chlorophyll from satellite images - particularly in coastal areas – yet the difference can be crucial for monitoring the marine environment.
  
  Chlorophyll is a pigment in marine phytoplankton (microscopic marine plants) and being able to identify it is important because it plays the same role in the ocean that grass plays on land, as a key part of the food chain and removing CO2 from the ocean.
  
  Using ML to solve this problem makes marine data as a whole more robust and researchers can be more certain about the results they are getting. For example, in coastal water around the UK there is a lot of sediment, so researchers need to be careful that what they think they are seeing is what they are actually seeing. Using ML to make this differentiation becomes a key part of their workflow.
  
  Jupyter Notebook Task 1 - ML for Retrieval of Water Quality Indicators: Naive Bayesian
  
  This Jupyter Notebook workflow guides you through classification of ocean colour data for the derivation of water quality relevant information. It will guide you through the process of training of a naive bayesian classifier on Sentinel-3 OLCI data in order to identify complex waters.
  
  You can view the video for this tutorial both here on FutureLearn as well as at the top of the Jupyter Notebook.
  
  For this Jupyter Notebook workflow tutorial please follow this link to go to the WEkEO Jupyter Hub or click on the link in the ‘See Also’ section below.
  
  If you have any questions, comments or would just like to discuss this notebook with your fellow learners, please write in the discussion below.
  
  Jupyter Notebook Task 2 - ML for Retrieval of Water Quality Indicators: Neural Network
  
  In this workflow example, we build on the basis that was introduced in the previous step, and introduce an auto-associative neural network approach that can be applied anywhere in the ocean.
  
  You can view the video for this tutorial both here on FutureLearn as well as at the top of the Jupyter Notebook.
  
  For this Jupyter Notebook workflow tutorial please follow this link to go to the WEkEO Jupyter Hub or click on the link in the ‘See Also’ section below.
Topic 4g - Using ML To Combine Water Quality Data from Different Satellites

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  Measuring chlorophyll concentration is the primary goal of most ocean colour (OC) studies. Sentinel-3 OLCI is the workhorse for this task, as it is the best OC radiometer we have in space. However, OLCI has a 300 meter resolution. Though not designed for marine work, the Sentinel-2 MSI radiometer has 10 meter resolution.
  
  In this video, Ben Loveday explains how ML can be used to seamlessly combine the two, delivering results that are much better than classical approaches. ML approaches like this are likely to become the mainstay of any coastal OC work. The technique is highly relevant to any researcher or agency that is investigating or monitoring the coastal or inland water domain.
  
  Jupyter Notebook Task: Chlorophyll Retrievals from Sentinel-2 and Sentinel-3 OLCI data
  
  For this Jupyter Notebook task, you will have a go at using machine learning for chlorophyll-a retrievals. The ML algorithm used for this task is the Mixture Density Network (MDN) which is trained and validated using a sizeable database of co-located chlorophyll-a measurements and in situ hyperspectral radiometric data resampled to simulate the Multispectral Instrument (MSI) and the Ocean and Land Color Imager (OLCI) onboard Sentinel-2A/B and Sentinel-3A/B, respectively.
  
  You can view the video for this tutorial both here on FutureLearn as well as at the top of the Jupyter Notebook.
  
  For this Jupyter Notebook workflow tutorial please follow this link to go to the WEkEO Jupyter Hub or click on the link in the ‘See Also’ section below.
  
  If you have any questions, comments or would just like to discuss this Notebook with your fellow learners, please write in the discussion below.
Topic 4h - Using Copernicus Data and Machine Learning To Monitor Marine Protected Areas

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  Marine Protected Areas (MPAs) are areas of the ocean that are set aside for long-term conservation aims. They can come in many forms, ranging from wildlife refuges to research areas. Examples of MPAs include Cook Islands Marine Park and Papahānaumokuākea Marine National Monument in the US, and there are around 3150 MPAs in the EU Member States under the Birds and Habitats Directives (SPAs and pSCIs) covering a surface of over 550,000 km², which is about 10% of the total marine area of the Member States.
  
  It is vital that these areas can be monitored so that we can continue to protect and preserve their marine ecosystem and biodiversity, and one way we can do this is with satellite and in situ data, and machine learning.
  
  An example of this in action is the Marine Analyst, which is an online marine data access platform to help people access detailed data on marine parameters, particularly in MPAs. It includes Copernicus satellite and in situ data and was developed during a Copernicus hackathon in Tallinn, Estonia, and it is running on WEkEO. It uses machine learning to provide some of the parameters for people to use to monitor the health of marine protected areas.
  
  Data Insights: In Conversation with Dr Hayley Evers-King
  
  In this second Data Insight, we hear from Dr Hayley Evers-King, Marine Applications Expert at EUMETSAT.
End of Week 4 DC

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Jupyter Notebook Task: AI for ship classification

Jupyter Notebook Task 1 - ML for Retrieval of Water Quality Indicators: Naive Bayesian

Jupyter Notebook Task 2 - ML for Retrieval of Water Quality Indicators: Neural Network

Jupyter Notebook Task: Chlorophyll Retrievals from Sentinel-2 and Sentinel-3 OLCI data

Data Insights: In Conversation with Dr Hayley Evers-King