# ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery
###### Abstract
In particular, Figure 1a shows the area as it appears from very high-resolution (50 cm) Pleiades satellite image; Figure 1b shows the imperviousness map at 10 m resolution realised by on-plane analysis performed by the Italian National Institute for Environmental Protection and Research (ISPRA ([[https://www.isprambiente.gov.it/](https://www.isprambiente.gov.it/)]([https://www.isprambiente.gov.it/](https://www.isprambiente.gov.it/)))); Figure 1c reports a 10 m imperviousness map realised by the Environmental Systems Research Institute (ESRI) using artificial intelligence algorithms; finally, Figure 1d shows a 10 m imperviousness map currently available on the Google Earth Engine under the name of \"DynamicWorld\", automatically generated using deep learning on Sentinel-2 imagery [6]. Furthermore, recent years have seen an enormous increase in the number of web-based applications leveraging techniques derived from geographic information systems (GIS). Even though it should be good news to have access to spatial data as well as advanced mapping and spatial analysis over the Internet as a critical point to pursue reduction of the distance between data, information and decision-makers, it has often been observed that many publicly available map layers are actually only accessible by people skilled with GIS. In this work, we want to tackle these problems by assembling a novel dataset, experimenting with a DL architecture designed to take advantage of Sentinel-2 multi-spectral data, and integrating the whole process in a proof-of-concept web application. In particular, the main contributions of this work can be summarised as follows:  
* To address the difficulties of the current solutions in the urban context, a new dataset has been gathered using an authoritative imperviousness map (ISPRA) as ground truth. ISPRA is a public institute, part of the Italian Ministry for the Environment, Territory and Sea, promoting and supporting scientific, technical and research functions as well as assessment, monitoring and control activities. Among other functions, it provides several land-cover maps, including the imperviousness map used in this work. These maps are produced semi-automatically from data provided by several European projects and authoritative data available for Italian territory, and are generated and released yearly;
* We introduce _Refuse_, a new DL architecture for impervious surface extraction based on a U-Net backbone [7], residual blocks (**Re**) [8] and the FuseNet principle (**Fuse**) [9] to take advantage of Sentinel-2 multi-spectral bands despite their different spatial resolutions. We also compared the performance of the proposed approach against some state-of-the-art CNNs;
* We integrated imperviousness inference and visualization into a GIS web application with a user-friendly interface for users without specific GIS competencies, implementing an inference pipeline leveraging modern distributed parallel computing and MLOPs best practices. This enables fast deployment of the solution on HPC or cloud computing systems, ensuring high scalability. The rest of the paper is structured as follows: Section 2 reports some related works; the process of the dataset generation, the proposed DL approach for impervious surface extraction and the web tool are described in Section 3; Section 4 describes the experimental setup; Section 5 reports and analyses obtained results while Section 6 provides some conclusions and future perspectives.