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Remote Sens. 2016, 8(4), 299; doi:10.3390/rs8040299

Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas

European Commission, Joint Research Center, Institute for Protection and Security of the Citizen, Global Security and Crisis Management Unit, Via Enrico Fermi 2749, I-21027 Ispra, Italy
These authors contributed equally to this work.
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Academic Editors: Clement Atzberger and Prasad S. Thenkabail
Received: 29 December 2015 / Revised: 18 March 2016 / Accepted: 25 March 2016 / Published: 1 April 2016
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Abstract

Monitoring of the human-induced changes and the availability of reliable and methodologically consistent urban area maps are essential to support sustainable urban development on a global scale. The Global Human Settlement Layer (GHSL) is a project funded by the European Commission, Joint Research Centre, which aims at providing scientific methods and systems for reliable and automatic mapping of built-up areas from remote sensing data. In the frame of the GHSL, the opportunities offered by the recent availability of Sentinel-2 data are being explored using a novel image classification method, called Symbolic Machine Learning (SML), for detailed urban land cover mapping. In this paper, a preliminary test was implemented with the purpose of: (i) assessing the applicability of the SML classifier on Sentinel-2 imagery; (ii) evaluating the complementarity of Sentinel-1 and Sentinel-2; and (iii) understanding the added-value of Sentinel-2 with respect to Landsat for improving global high-resolution human settlement mapping. The overall objective is to explore areas of improvement, including the possibility of synergistic use of the different sensors. The results showed that noticeable improvement of the quality of the classification could be gained from the increased spatial detail and from the thematic contents of Sentinel-2 compared to the Landsat derived product as well as from the complementarity between Sentinel-1 and Sentinel-2 images. View Full-Text
Keywords: Sentinel-2; Landsat; Sentinel-1; global human settlement layer; symbolic machine learning; land cover mapping; automatic classification Sentinel-2; Landsat; Sentinel-1; global human settlement layer; symbolic machine learning; land cover mapping; automatic classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Pesaresi, M.; Corbane, C.; Julea, A.; Florczyk, A.J.; Syrris, V.; Soille, P. Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas. Remote Sens. 2016, 8, 299.

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