Situational Awareness of Large Infrastructures Using Remote Sensing: The Rome–Fiumicino Airport during the COVID-19 Lockdown

Abstract

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.

Delving into Geospatial Data Services: Monitoring Earth for Covid-19 Impact Measure and Decision Making

“…Moreover, the NTL patterns at the entrances and exits of some expressways also increased during the epidemic, which helped implement the active monitoring and control measures for Covid-19. Therefore, monitoring NTL pattern changes in specific locations helps evaluate the urban planning process and preventive measures against the epidemic [40]. A drastic activity reduction over the apron areas directly connected with the terminal buildings was noticed in the study.…”

“…Christopher et al [39] explored the effects of dimming lights during a lockdown in China and discovered a 3%-24% decrease in illumination brightness. SAR images detect traffic patterns throughout the lockdown in Rome -Fiumicino International Airport [40]. The changes in economic activities and environmental impacts are studied using the NTL during the lockdown in the US [41].…”

“…The consequences of the Covid-19 lockdown in Spain are well-studied [43]. Pulella and Sica [40] demonstrate the potentiality of existing RS constellations for situational awareness applications. The spatial-temporal variations of LST before and after the lockdown, in response to the spread of Covid-19, for three major Indian cities located in different climate zones (New Delhi, Hyderabad, and Mumbai) are studied [44].…”

Spatiotemporal patterns of the COVID-19 control measures impact on industrial production in Wuhan using time-series earth observation data

“…These studies have mainly focused on planetary energy balance ( Yi et al., 2020 ), air and environment quality ( Lian et al., 2020 ; Rumpler et al., 2020 ; Liu et al., 2020c ; Chakraborty & Maity, 2020 ; Sathe et al., 2021 ; Wang & Li, 2021 ; Latif et al., 2021 ; Benchrif et al., 2021 ), global trade ( Vidya & Prabheesh, 2020 ; Guan et al., 2020 ), travel and tourism industry ( Pulella & Sica, 2021 ; Tian et al., 2021 ; Shakibaei et al., 2021 ; Liang et al., 2021 ), social and economic activity ( Ehlert, 2021 ; Maliszewska et al., 2020 ), and public behaviors ( Si et al., 2021 ). Multi-source data were used, including social economic statistical data ( Sha et al., 2020 ; Parolin & Wimer, 2020 ), travelling data ( Kang et al., 2020b ; Jiang et al., 2021a ), mobile phone data ( Willberg et al., 2021 ; Xiong et al., 2020 ; Jia et al., 2020 ; Jadidi et al., 2021 ), observation and sensor data ( Pulella & Sica, 2021 ; Liu et al., 2020b ; Chen et al., 2021 ; Vîrghileanu et al., 2020 ; Basu et al., 2021 ), social media data ( Peng et al., 2020 ; Li et al., 2020a ; Zhu et al., 2020 ; Beria & Lunkar, 2021 ), and bibliometric data ( Benita, 2021 ; Das & Dutta, 2021 ; Kutela et al., 2021 ). The study scales ranged from globe ( Chakraborty & Maity, 2020 ; Laborde et al., 2020 ; Gupta et al., 2021 ), to continent and county ( Fanelli & Piazza, 2020 ; Shi et al., 2020 ; Liu et al., 2020a ; Kang et al., 2020b ), to city and district ( Tian et al., 2020 ; Ma et al., 2020 ; Liu et al., 2021 ).…”