# ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery
###### Abstract  
Continual mapping and monitoring of impervious surfaces are crucial activities to support sustainable urban management strategies and to plan effective actions for environmental changes. In this context, impervious surface coverage is increasingly becoming an essential indicator for assessing urbanization and environmental quality, with several works relying on satellite imagery to determine it. However, although satellite imagery is typically available with a frequency of 3-10 days worldwide, imperviousness maps are released at most annually as they require a huge human effort to be produced and validated. Attempts have been made to extract imperviousness maps from satellite images using machine learning, but (i) the scarcity of reliable and detailed ground truth (ii) together with the need to manage different spectral bands (iii) while making the resulting system easily accessible to the end users is limiting their diffusion. To tackle these problems, in this work we introduce a deep-learning-based approach to extract imperviousness maps from multi-spectral Sentinel-2 images leveraging a very detailed imperviousness map realised by the Italian department for environment protection as ground truth. We also propose a scalable and portable inference pipeline designed to easily scale the approach, integrating it into a web-based Geographic Information System (GIS) application. As a result, even non-expert GIS users can quickly and easily calculate impervious surfaces for any place on Earth (accuracy \(>\) 95%), with a frequency limited only by the availability of new satellite images. FuseNet; U-Net; ResNet; impervious; land cover; remote sensing; deep learning; CNN; Sentinel-2 +
Footnote †: journal: _Future Internet_  
(also commonly known as remote sensing). Although carrying out on-place surveys produces more comprehensive and authoritative outcomes, performing it is an expensive and time-consuming process, involving the movement of people and tools. Therefore, automating this process is extremely useful for reducing the amount of work and to limit the associated costs. One of the aspects making automatic analysis non-trivial is that impervious areas are usually made of different construction materials, resulting in significantly variegated spectral signatures, and spatial patterns [3]. As for many other computer vision tasks, in recent years, Deep-Learning (DL) algorithms, and in particular Convolutional Neural Networks (CNNs), have been showing promising results in land-cover classification [4; 5]. This has been firmly pushed by satellite images usually existing also in visible-light (RGB) channels, thus allowing for the leverage of DL models designed for tasks requiring the analysis of natural images. However, the situation is dramatically different when it comes to imperviousness analysis, due to (i) the lack of high-quality labelled datasets and (ii) the fact that RGB spectral data are usually not the most suited for the task. In addition, current solutions providing land-cover maps through automatic algorithms show some issues in addressing the high granularity of the data in an urban context, losing the essential details needed for accurate analysis. To better understand the extent of this problem in urban areas, in Figure 1 we compare different imperviousness maps for the same land portion covering a small area in the city centre of Rome, Italy.