Project Description

• Handling and processing in situ data acquired from ESA IoT air quality devices (so called Air Quality Platform - AQP) and official ARPA stations
• Time/space co-locating with EO satellite data (Sentinel-5p) to assess their correlation.
• Machine learning techniques will be investigated to propose joint IoT/EO fusion products

Tools

• Electronic Laboratory
• Catboots library
• ML Python libraries

Development outputs obtained during the project

• Correlation between Satellite data (Sentinel-5p), in-situ Data (ARPA Lazio) and IoT data (AQP system)
• Thesis degree: Evaluation of environmental parameters with Copernicus Sentinel-5P data, ARPA, AQP in situ data and Deep Learning techniques