# ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery
## 3 Materials and Methods
### Imperviousness Dataset Generation  
This section describes the methodology we used to create the dataset to be used to train our deep-learning model for generating imperviousness maps from Sentinel-2 satellite images (dataset openly available in Zenodo at [[https://doi.org/10.5281/zenodo.7058860](https://doi.org/10.5281/zenodo.7058860)]([https://doi.org/10.5281/zenodo.7058860](https://doi.org/10.5281/zenodo.7058860)) accessed on 22 September 2022). The _Sentinel-2_ platform consists of two satellites equipped with sensors able to acquire images with 13 spectral bands ranging from the visible range to the short-wave infrared. Each sensor has a different spatial resolution, with RGB bands and near-infrared (NIR) showing the highest one (i.e., 10 m). With a 12-bit radiometric resolution, the image can be collected from 0 to 4095 possible light intensity values, enabling the satisfactory identification of minor variations in reflected or emitted energy. The lack of labelled data poses a serious obstacle to developing deep-learning algorithms that detect impermeable surfaces. Most available imperviousness maps typically have a coarse spatial resolution that does not adequately emphasize green spaces in urban settings. In this work, we used the soil consumption map covering Italy provided by ISPRA for 2017 with a 10 m spatial resolution. These data were built by merging regional Land Use Land Cover (LULC) maps, in situ data provided to ISPRA by Regional Environmental Agencies, Copernicus HRL Imperviousness products, OpenStreetMap, and local supplementary data. The map consists of a hierarchical classification ID with each pixel having up to three digits class: the first digit, starting from the left, describes whether a point is consumed soil (i.e., a value of 1) or not consumed soil (i.e., a value of 2), while the other two optional digits can specify the class with more detail (e.g., 112 stands for \"soil consumed by asphalt roads\"). Since we were interested in the segmentation between impervious and non-impervious surfaces, only the first digit was considered to label the data. The flowchart in Figure 4 illustrates the process used in this work for generating labelled training, evaluation, and testing data. Sentinel-2 granules are organized in a tiling, partially overlapping grid. As for other image-processing domains, having a dataset representative of the population is crucial. As the authors of this paper are Italian, we decided to select Sentinel-2 tiles covering particular zones of the Italian peninsula containing a variegate distribution of hills, waters, plains, mountains, etc., both in urban and suburban realms. To further increase the variance, we extracted images recorded all over the year, acquiring at least one image per season. Nonetheless, it is worth noting that only images for 2017 were considered, to minimize labelling mismatch with the available ground truth. Table 1 lists Sentinel-2 products used for the dataset generation while Figure 5 shows selected patches distribution along the Italian peninsula. Figure 3: Inference Pipeline scheme. On the left, the process of the Sentinel-2 satellite imagery retrieval. On the right, the execution of the prediction. As detailed in Section 3.3, we choose Docker to set up the image for the inference. [PERSON] makes the solution scalable by parallelising the execution.