Situational Awareness of Large Infrastructures Using Remote Sensing: The Rome–Fiumicino Airport during the COVID-19 Lockdown
Situational awareness refers to the process of aggregating spatio-temporal variables and measurements from different sources, aiming to improve the semantic outcome. Remote Sensing satellites for Earth Observation acquire key variables that, when properly aggregated, can provide precious insights ab...
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MDPI AG
2021-01-01
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Series: | Remote Sensing |
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Online Access: | https://www.mdpi.com/2072-4292/13/2/299 |
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author | Andrea Pulella Francescopaolo Sica |
author_facet | Andrea Pulella Francescopaolo Sica |
author_sort | Andrea Pulella |
collection | DOAJ |
description | Situational awareness refers to the process of aggregating spatio-temporal variables and measurements from different sources, aiming to improve the semantic outcome. Remote Sensing satellites for Earth Observation acquire key variables that, when properly aggregated, can provide precious insights about the observed area. This article introduces a novel automatic system to monitor the activity levels and the operability of large infrastructures from satellite data. We integrate multiple data sources acquired by different spaceborne sensors, such as Sentinel-1 Synthetic Aperture Radar (SAR) time series, Sentinel-2 multispectral data, and Pleiades Very-High-Resolution (VHR) optical data. The proposed methodology exploits the synergy between these sensors for extracting, at the same time, quantitative and qualitative results. We focus on generating semantic results, providing situational awareness, and decision-ready insights. We developed this methodology for the COVID-19 Custom Script Contest, a remote hackathon funded by the European Space Agency (ESA) and the European Commission (EC), whose aim was to promote remote sensing techniques to monitor environmental factors consecutive to the spread of the Coronavirus disease. This work focuses on the Rome–Fiumicino International Airport case study, an environment significantly affected by the COVID-19 crisis. The resulting product is a unique description of the airport’s area utilization before and after the air traffic restrictions imposed between March and May 2020, during Italy’s first lockdown. Experimental results confirm that the proposed algorithm provides remarkable insights for supporting an effective decision-making process. We provide results about the airport’s operability by retrieving temporal changes at high spatial and temporal resolutions, together with the airplane count and localization for the same period in 2019 and 2020. On the one hand, we detected an evident change of the activity levels on those airport areas typically designated for passenger transportation, e.g., the one close to the gates. On the other hand, we observed an intensification of the activity levels over areas usually assigned to landside operations, e.g., the one close to the hangar. Analogously, the airplane count and localization have shown a redistribution of the airplanes over the whole airport. New parking slots have been identified as well as the areas that have been dismissed. Eventually, by combining the results from different sensors, we could affirm that different airport surface areas have changed their functionality and give a non-expert interpretation about areas’ usage. |
first_indexed | 2024-03-09T04:36:08Z |
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institution | Directory Open Access Journal |
issn | 2072-4292 |
language | English |
last_indexed | 2024-03-09T04:36:08Z |
publishDate | 2021-01-01 |
publisher | MDPI AG |
record_format | Article |
series | Remote Sensing |
spelling | doaj.art-b0c869c0c50b45d38e65c47ced7fc9782023-12-03T13:28:34ZengMDPI AGRemote Sensing2072-42922021-01-0113229910.3390/rs13020299Situational Awareness of Large Infrastructures Using Remote Sensing: The Rome–Fiumicino Airport during the COVID-19 LockdownAndrea Pulella0Francescopaolo Sica1German Aerospace Center (DLR), Microwaves and Radar Institute, Münchener Straße 20, 82234 Weßling, GermanyGerman Aerospace Center (DLR), Microwaves and Radar Institute, Münchener Straße 20, 82234 Weßling, GermanySituational awareness refers to the process of aggregating spatio-temporal variables and measurements from different sources, aiming to improve the semantic outcome. Remote Sensing satellites for Earth Observation acquire key variables that, when properly aggregated, can provide precious insights about the observed area. This article introduces a novel automatic system to monitor the activity levels and the operability of large infrastructures from satellite data. We integrate multiple data sources acquired by different spaceborne sensors, such as Sentinel-1 Synthetic Aperture Radar (SAR) time series, Sentinel-2 multispectral data, and Pleiades Very-High-Resolution (VHR) optical data. The proposed methodology exploits the synergy between these sensors for extracting, at the same time, quantitative and qualitative results. We focus on generating semantic results, providing situational awareness, and decision-ready insights. We developed this methodology for the COVID-19 Custom Script Contest, a remote hackathon funded by the European Space Agency (ESA) and the European Commission (EC), whose aim was to promote remote sensing techniques to monitor environmental factors consecutive to the spread of the Coronavirus disease. This work focuses on the Rome–Fiumicino International Airport case study, an environment significantly affected by the COVID-19 crisis. The resulting product is a unique description of the airport’s area utilization before and after the air traffic restrictions imposed between March and May 2020, during Italy’s first lockdown. Experimental results confirm that the proposed algorithm provides remarkable insights for supporting an effective decision-making process. We provide results about the airport’s operability by retrieving temporal changes at high spatial and temporal resolutions, together with the airplane count and localization for the same period in 2019 and 2020. On the one hand, we detected an evident change of the activity levels on those airport areas typically designated for passenger transportation, e.g., the one close to the gates. On the other hand, we observed an intensification of the activity levels over areas usually assigned to landside operations, e.g., the one close to the hangar. Analogously, the airplane count and localization have shown a redistribution of the airplanes over the whole airport. New parking slots have been identified as well as the areas that have been dismissed. Eventually, by combining the results from different sensors, we could affirm that different airport surface areas have changed their functionality and give a non-expert interpretation about areas’ usage.https://www.mdpi.com/2072-4292/13/2/299situational awarenessCOVID-19activity monitoringSynthetic Aperture RadarSentinel-1Sentinel-2 |
spellingShingle | Andrea Pulella Francescopaolo Sica Situational Awareness of Large Infrastructures Using Remote Sensing: The Rome–Fiumicino Airport during the COVID-19 Lockdown Remote Sensing situational awareness COVID-19 activity monitoring Synthetic Aperture Radar Sentinel-1 Sentinel-2 |
title | Situational Awareness of Large Infrastructures Using Remote Sensing: The Rome–Fiumicino Airport during the COVID-19 Lockdown |
title_full | Situational Awareness of Large Infrastructures Using Remote Sensing: The Rome–Fiumicino Airport during the COVID-19 Lockdown |
title_fullStr | Situational Awareness of Large Infrastructures Using Remote Sensing: The Rome–Fiumicino Airport during the COVID-19 Lockdown |
title_full_unstemmed | Situational Awareness of Large Infrastructures Using Remote Sensing: The Rome–Fiumicino Airport during the COVID-19 Lockdown |
title_short | Situational Awareness of Large Infrastructures Using Remote Sensing: The Rome–Fiumicino Airport during the COVID-19 Lockdown |
title_sort | situational awareness of large infrastructures using remote sensing the rome fiumicino airport during the covid 19 lockdown |
topic | situational awareness COVID-19 activity monitoring Synthetic Aperture Radar Sentinel-1 Sentinel-2 |
url | https://www.mdpi.com/2072-4292/13/2/299 |
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