AI for Medicanes Science

Applying advanced ML techniques

Satellites gather vast amounts of environmental data daily, making remote sensing a key player in Big Data and Digital Twin initiatives. To efficiently analyze this data, experts are leveraging artificial intelligence (AI) tools, particularly machine learning (ML).

The Role of Machine Learning

Machine learning algorithms are capable of learning from data, managing complexity, and integrating information from various sensors across different platforms. They utilize the full spectrum of available channels to solve complex, non-linear problems. However, ML implementation in remote sensing is relatively new and faces with some challenges:

  • Data Availability: Training ML algorithms requires datasets that are accessible, accurate, and well-developed.
  • Computational Resources: Processing large datasets demands significant computational power and storage, which can be managed using GPUs and cloud infrastructure.
  • Data Uncertainty: Remotely sensed data inherently contains uncertainties and errors, complicating the training and testing of ML models.

    • Applications of Machine Learning in Remote Sensing

    Machine learning has a wide range of applications in remote sensing, including environmental parameter estimation and supporting future satellite missions with End-to-End simulations that could be used in the project:

  • Precipitation Retrieval: ML algorithms adapted for precipitation retrieval from microwave radiometers, effectively handling the complex, non-linear relationships between multi-channel microwave measurements and precipitation and new deep learning techniques applied to infrared measurements are also on the rise.
  • Tropical Cyclone Intensity Prediction: Deep learning, particularly Convolutional Neural Networks (CNNs), has shown great promise in predicting tropical cyclone (TC) intensity. CNNs can recognize features across multiple images, identifying patterns and extracting intensity-related information.