# ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery
## 6 Conclusions  
In this study, we introduced a deep-learning-based method for extracting imperviousness maps from multi-spectral Sentinel-2 pictures leveraging bands with different spatial resolutions without the need for rescaling or other adaptations. Additionally, the proposed approach has also been made available through a portable and scalable inference pipeline, easily pluggable within a web-based GIS application. The aim is to support the generation of imperviousness maps as soon as new satellite images are available for a fast, effective and reliable analysis of human environmental impact. To achieve this, one of the biggest challenges was the lack of a labelled dataset, with a temporal and spatial granularity, as well as precision, suited for the task. To address this problem, we gathered a new dataset using the ISPRA imperviousness map as the ground-truth raster. In particular, as we used the soil consumption map covering Italy provided by ISPRA for 2017, we generated the used dataset by selecting Sentinel-2 tiles covering different parts of the Italian peninsula and for different periods of 2017 to include several soil characteristics. The proposed approach is a deep-learning architecture designed for impervious surface extraction based on a U-Net backbone and leveraging residual blocks and the FuseNet principles (here the name) to effectively take advantage of Sentinel-2 multi-spectral bands despite their different spatial resolutions. To evaluate the effectiveness of the proposed approach, we compared the performance of the ReFuse architecture against some state-of-the-art CNNs. For the sake of completeness, we analysed the effectiveness of some variants obtained by changing the used encoder and \(\lor\) or considered bands, showing how the use of residual connections and the selected bands ensures the best performance. Nonetheless, all the considered competitors are outperformed by ReFuse. Additionally, as the work aims to realise a simple and effective tool, we integrated the proposed approach into a GIS web application. Figure 2 shows how requesting data for a specific area of interest can be simple, even for non-experts. In Figure 13, we share a web page we realised where the impervious data are presented to the end user using a map with a hexagonal grid. The grid was computed with a zonal statistics process applied to the imperviousness map computed through the proposed approach. The whiter areas represent the areas with the most impervious surfaces; vice versa, greener hexagons are the zones with the highest presence of trees, parks, and gardens. The image shows the map of impervious surfaces for the city of Turin in Italy. In particular, it shows how the areas with  
Figure 12: Semantic mapping with ReFuse model for two areas in the city of Turin. (**a**,**e**) Optical images extracted from Google Earth. (**b**,**f**) Sentinel-2 RGB images. (**c**,**g**) Ground truth extracted from ISPRA Soil consumption map. In red, imperviousness surfaces. (**d**,**h**) Predicted maps with the ReFuse model. Despite errors in the ground-truth labels (see image (**c**)), the network correctly detects the green infrastructure within the area, i.e., trees, grass and parks (see image (**d**)). the highest content of impervious surfaces in the city centre can be easily identified, i.e., white hexagons. In conclusion, the proposed approach shows how deep-learning, MLOPS and web-based application can effectively be used for a social-good application, such as imperviousness classification, in a simple and intuitive manner. The applicability of the proposed approach to different land-cover classification tasks will be tested to analyse the generalization capability of the approach across different applications. Finally, other experiments will be conducted, considering the time variable and extending the proposed dataset with new data sources, such as Sentinel-1 SAR satellite imagery. Conceptualization, G.G., S.M., G.L. and C.S. ; methodology, G.G. and S.M. ; software, G.G. ; validation, G.G., S.M., G.L. and C.S.