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Article

Deep Learning with Open Data for Desert Road Mapping

1
European Space Agency (ESA), Earth Observation Programmes, Future Systems Department, 00044 Frascati, Italy
2
European Union Satellite Centre (SatCen), 28850 Madrid, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(14), 2274; https://doi.org/10.3390/rs12142274
Received: 10 June 2020 / Revised: 2 July 2020 / Accepted: 10 July 2020 / Published: 15 July 2020
The availability of free and open data from Earth observation programmes such as Copernicus, and from collaborative projects such as Open Street Map (OSM), enables low cost artificial intelligence (AI) based monitoring applications. This creates opportunities, particularly in developing countries with scarce economic resources, for large–scale monitoring in remote regions. A significant portion of Earth’s surface comprises desert dune fields, where shifting sand affects infrastructure and hinders movement. A robust, cost–effective and scalable methodology is proposed for road detection and monitoring in regions covered by desert sand. The technique uses Copernicus Sentinel–1 synthetic aperture radar (SAR) satellite data as an input to a deep learning model based on the U–Net architecture for image segmentation. OSM data is used for model training. The method comprises two steps: The first involves processing time series of Sentinel–1 SAR interferometric wide swath (IW) acquisitions in the same geometry to produce multitemporal backscatter and coherence averages. These are divided into patches and matched with masks of OSM roads to form the training data, the quantity of which is increased through data augmentation. The second step includes the U–Net deep learning workflow. The methodology has been applied to three different dune fields in Africa and Asia. A performance evaluation through the calculation of the Jaccard similarity coefficient was carried out for each area, and ranges from 84% to 89% for the best available input. The rank distance, calculated from the completeness and correctness percentages, was also calculated and ranged from 75% to 80%. Over all areas there are more missed detections than false positives. In some cases, this was due to mixed infrastructure in the same resolution cell of the input SAR data. Drift sand and dune migration covering infrastructure is a concern in many desert regions, and broken segments in the resulting road detections are sometimes due to sand burial. The results also show that, in most cases, the Sentinel–1 vertical transmit–vertical receive (VV) backscatter averages alone constitute the best input to the U–Net model. The detection and monitoring of roads in desert areas are key concerns, particularly given a growing population increasingly on the move. View Full-Text
Keywords: synthetic aperture radar; SAR; Sentinel–1; Open Street Map; deep learning; U–Net; desert; road; infrastructure; mapping; monitoring synthetic aperture radar; SAR; Sentinel–1; Open Street Map; deep learning; U–Net; desert; road; infrastructure; mapping; monitoring
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  • Externally hosted supplementary file 1
    Link: https://github.com/ESA-PhiLab/infrastructure
    Description: Github repository containing all scripts that were used in this research, including the Bash files and GPT graphs for the Sentinel-1 data processing, and the Python code for the deep learning workflow available in a Jupyter Notebook. This is maintained by the ESA-Φ-Lab.
MDPI and ACS Style

Stewart, C.; Lazzarini, M.; Luna, A.; Albani, S. Deep Learning with Open Data for Desert Road Mapping. Remote Sens. 2020, 12, 2274. https://doi.org/10.3390/rs12142274

AMA Style

Stewart C, Lazzarini M, Luna A, Albani S. Deep Learning with Open Data for Desert Road Mapping. Remote Sensing. 2020; 12(14):2274. https://doi.org/10.3390/rs12142274

Chicago/Turabian Style

Stewart, Christopher, Michele Lazzarini, Adrian Luna, and Sergio Albani. 2020. "Deep Learning with Open Data for Desert Road Mapping" Remote Sensing 12, no. 14: 2274. https://doi.org/10.3390/rs12142274

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