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.
Highlights
There is a strong demand for evidence-based global policies in the economic, social, environmental and disaster risk reduction spheres
Among the main advantages of Sentinel-2, which make the satellite highly suitable for mapping and monitoring human settlements at a global level, it is the combination of the wide swath and the frequent revisiting time at high spatial resolutions
The application of the Symbolic Machine Learning (SML) classifier to Sentinel-2 data is encouraging because of improvements in terms of spatial detail and thematic contents with respect to the Landsat derived product. It demonstrates the capacity of the SML methodology to handle different sets of input features such as radiometric, textural and morphological descriptors derived from Sentinel-2 data
Summary
There is a strong demand for evidence-based global policies in the economic, social, environmental and disaster risk reduction spheres. The understanding of the global human settlements and urban expansion are critical for a large number of issues including housing and urban development, poverty reduction, sustainable development, climate change, biodiversity conservation, ecosystem services provision and disaster management [2]. Several indicators, such as the access to infrastructure, pressure on biodiversity, urban planning and management [3,4], exposure and vulnerability to natural hazards, can be derived from the information on human settlements. Earth observation is a unique source of information for deriving globally-consistent and evidence-based Data from satellite imagery and new technologies for processing earth observation data facilitate the study of population distribution on a global scale.
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