Environmental noise mapping using Earth observation (EO)

The research aims to uncover the relationship between modeled environmental noise and EO data. In particular, you will implement an AI model, Machine learning (ML) or Deep learning (DL), to predict noise in a geographic area.

Data:

• EO data, e.g., sentinel, night lights images, vegetation
• Modeled noise maps

Methods:

• Machine learning methods: supervised, unsupervised, or their combination.
• Collaborative planning workshop, maptables.

Workflow:

The suggested workflow of this research follows:

1. Literature review of state-of-the-art ML/DL models for noise prediction using EO
2. Data acquisition
3. Implementation of the model
4. Assessment of model performance
5. Critical reflection on the model and its utility in consultation with key stakeholders.

This topic requires an interest in noise pollution and programming skills as you will work with Python. Also, there is ample room and opportunity to shape this research project in consultation with the supervisors, and we expect students who choose this topic to take the initiative to do so.