# ReFuse: Generating Imperviousness Maps from Multi-Spectral Sentinel-2 Satellite Imagery
## 2 Related Works
The decoder increases the resolution of the feature representations to create a full-resolution segmentation map. U-Net added several skip connections, which concatenates the feature maps of the encoder part with the mirrored feature maps in the contracting path. In [26], [PERSON] et al. experimented with the use of a CNN to extract impervious surfaces through Worldview-2 and airborne LiDAR. The findings showed that 3D-CNN had more ability to extract features than SVM since it used pixel-level spatial information as well as texture. Ref. [27] uses a deep-learning approach to extract impervious surfaces from WorldView 2 and Pleiades-1A datasets automatically. In [28], the authors conducted a comparative study for the impervious surface estimation mixing optical and SAR data; experimental results indicated the effectiveness of the proposed deep convolutional network, which exhibited a better accuracy outperforming other benchmark methods. In [29], [PERSON] et al. proposed a solution based on a deep CNN to map impervious surfaces in town-rural areas using China's GF-2 Imagery. They showed the effectiveness of deep models and how transfer learning could significantly boost overall accuracy. Fewer studies are available on using Sentinel-2 imagery for built-up ISA extraction and even less with a deep-learning approach. In [30] the authors assessed the feasibility of using Sentinel-2 images for this task by means of an artificial neural network. In [31], using Sentinel-2 satellite imagery, a CNN was employed as a deep feature extractor, and the classification was made by means of a random forest classifier. Similarly, in [32] the authors compared different machine-learning and deep-learning algorithms for the land-cover classification, with a CNN showing the best performance in the classification of impervious areas. Deep learning has also been investigated for multi-sensor and multi-modal image segmentation. Multi-modal fusion strategies are of great interest in the field of RS classification since satellite images usually consist of multi-spectral content. Similarly, extensive research has also been conducted to combine heterogeneous data (multi-sensor fusion), such as optical images with Synthetic Aperture Radar (SAR) and LiDAR data. Inboth cases, fusion models help reduce confusion from spectral heterogeneity in landscapes and enhance classification accuracy. On this line, in [33] the authors use existing CNNs (FCN or SegNet) as a base network to experiment with different data fusion strategies, both in early and late fusion fashion. In the same way, Ref. [34] explores how deep fully convolutional networks can be modified to handle multi-modal and multi-scale remote-sensing data for semantic labelling. To this aim, the authors extended the FuseNet architecture [9] by considering two branches, one trained with IR-R-G bands and one with Normalised Digital Surface Model (NDSM), Digital Surface Model (DSM) and NDVI data. The proposed approaches outperformed a SegNet trained only on IR-R-G bands, thus proving the effectiveness of using multi-spectral data for remote-sensing classification.