Urban Sprawl and COVID-19 Impact Analysis by Integrating Deep Learning with Google Earth Engine

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 9; p. 2038
• Main Authors: Zarro, Chiara, Cerra, Daniele, Auer, Stefan, Ullo, Silvia Liberata, Reinartz, Peter
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2022
• Subjects: Algorithms; Application programming interface; Case studies; Change detection; Cloud computing; Copernicus; Coronaviruses; COVID-19; Cultural heritage; data fusion; Datasets; Decision making; Decision support systems; Deep learning; Earth; Image resolution; Impact analysis; Intelligence; Land use; Libraries; Machine learning; Metropolitan areas; Monitoring; Pandemics; Radar data; Remote sensing; Satellites; Sentinel-2; Severe acute respiratory syndrome coronavirus 2; Spatial data; Synthetic aperture radar; Urban areas; Urban sprawl; Urbanization; Water quality
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14092038

Timely information on land use, vegetation coverage, and air and water quality, are crucial for monitoring and managing territories, especially for areas in which there is dynamic urban expansion. However, getting accessible, accurate, and reliable information is not an easy task, since the significant increase in remote sensing data volume poses challenges for the timely processing and analysis of the resulting massive data volume. From this perspective, classical methods for urban monitoring present some limitations and more innovative technologies, such as artificial-intelligence-based algorithms, must be exploited, together with performing cloud platforms and ad hoc pre-processing steps. To this end, this paper presents an approach to the use of cloud-enabled deep-learning technology for urban sprawl detection and monitoring, through the fusion of optical and synthetic aperture radar data, by integrating the Google Earth Engine cloud platform with deep-learning techniques through the use of the open-source TensorFlow library. The model, based on a U-Net architecture, was applied to evaluate urban changes in Phoenix, the second fastest-growing metropolitan area in the United States. The available ancillary information on newly built areas showed good agreement with the produced change detection maps. Moreover, the results were temporally related to the appearance of the SARS-CoV-2 (commonly known as COVID-19) pandemic, showing a decrease in urban expansion during the event. The proposed solution may be employed for the efficient management of dynamic urban areas, providing a decision support system to help policy makers in the measurement of changes in territories and to monitor their impact on phenomena related to urbanization growth and density. The reference data were manually derived by the authors over an area of approximately 216 km2, referring to 2019, based on the visual interpretation of high resolution images, and are openly available.