Monitoring the Impact of Large Transport Infrastructure on Land Use and Environment Using Deep Learning and Satellite Imagery

Large-scale infrastructure, such as China–Europe Railway Express (CER-Express), which connects countries and regions across Asia and Europe, has a potentially profound effect on land use, as evidenced by changes in land cover along the railway. To ensure sustainable development of such infrastructur...

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Bibliographic Details
Published inRemote sensing (Basel, Switzerland) Vol. 14; no. 10; p. 2494
Main Authors Pavlovic, Marko, Ilic, Slobodan, Antonic, Nenad, Culibrk, Dubravko
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2022
Subjects
Aerosols
Artificial intelligence
Deep learning
Earth
environment
Environmental monitoring
Image classification
Infrastructure
Initiatives
Land cover
Land management
Land use
Machine learning
Remote monitoring
Remote sensing
Satellite imagery
Satellites
Sustainable development
transport infrastructure
Unmanned aerial vehicles
Europe
China
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Summary:Large-scale infrastructure, such as China–Europe Railway Express (CER-Express), which connects countries and regions across Asia and Europe, has a potentially profound effect on land use, as evidenced by changes in land cover along the railway. To ensure sustainable development of such infrastructure and appropriate land administration, effective ways to monitor and assess its impact need to be developed. Remote sensing based on publicly available satellite imagery represents an obvious choice. In the study presented here, we employ a state-of-the-art deep-learning-based approach to automatically detect different types of land cover based on multispectral Sentinel-2 imagery. We then use these data to conduct and present a study of the changes in land use in two geopolitically diverse regions of interest (in Serbia and China and with and without CER-Express infrastructure) for the period of the last three years. Our results show that the standard image-patch-based land cover classification approaches suffer a significant drop in performance in our target scenario in which each pixel needs to be assigned a cove class, but still, validate the applicability of the proposed approach as a remote sensing tool to support the sustainable development of large infrastructure. We discuss the technical limitations of the proposed approach in detail and potential ways in which it can be improved.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14102494