# Mapping of Kill Erosion of the Middle Volga (Russia) Region Using Deep Neural Network
## 5 Conclusions  
Deep neural networks can effectively solve geomorphological problems, allowing large-scale mapping of erosion processes. For the first time in the world, a detailed assessment of the density of rill erosion and its relation to the main natural-anthropogenic factors is given. In this case, preparation of the training sample is the most time-consuming stage, taking 2/3 of the whole time of the study. The trained model allows highly efficient and at an acceptable level of recognition of the erosion network on high-resolution images, in our case using Sentinel-2 images. We investigated which Sentinel-2 bands allow the most efficient visual recognition at the preliminary stage. As a result of numerous experiments, the best \"readability\" of the image was demonstrated by a separate near-infrared band, winning in comparison with RGB-synthesis, synthesis of artificial colors in various combinations, and other channels, separately. Another sensitive point of the research is the choice of neural network architecture for semantic segmentation. Experiments were carried out with the most common architectures for such tasks, including the well-known U-Net architecture, as well as FPN, PSPNet, and LinkNet in combination with the most popular encoder groups, such as VGG, ResNet, SE-ResNet, ResNeXt, SE-ResNeXt, SENet154, DenseNet, Inception, MobileNet, and EfficientNet. The best recognition accuracy on the test set showed the FPN + EfficientNet combination; however, when applying the trained model, the recognition results were poor. The best results in terms of real-world applications were achieved using LinkNet + DenseNet (DenseNet201). In the future, it is planned to use the trained model to recognize the erosion network for 2019-2020 to assess the dynamics of erosion processes in time. The analysis made it possible to assess the key factors influencing the intensity of rill erosion processes at a high level of confidence. In some cases, existing knowledge about the influence of certain factors on soil erosion was confirmed. Particular interest is the possibility of mapping the territories with the highest risk of erosion processes due to natural-anthropogenic conditions, which will allow producing precise and effective  
Figure 21: QQ–plot of MANOVA between rill network and forest cover, anthropogenic load, prevailing soil type, and soil-forming rocks. measures to minimize the risks. Under the conditions of intensive agricultural development, no reduction of anthropogenic load can be discussed; however, proper crop rotations, regular sowing of perennial grasses, and proper plowing will reduce the existing risks many times. The use of generalized additive models (GAM) to analyze dependencies made it possible to describe complex relationships between the density of rill erosion and climate, geomorphological, and other geographic factors. Unfortunately, not all parameters could be used in the analysis; in the future, detailed modeling of the dependence of erosion processes on factors of soil freezing, and the intensity of precipitation, especially changes in intensity in time series, should be conducted. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/jigi11030197/s1](https://www.mdpi.com/article/10.3390/jigi11030197/s1)]([https://www.mdpi.com/article/10.3390/jigi11030197/s1](https://www.mdpi.com/article/10.3390/jigi11030197/s1)), Table S1: Prevailing soil type list; Table S2: Prevailing type of soil-forming rocks list. This research was funded by grant number MK-2005.2021.1.5. Not applicable. The data presented in this study are available on request from the corresponding author and partially available on the [[http://bassepr.kpfu.ru/](http://bassepr.kpfu.ru/)]([http://bassepr.kpfu.ru/](http://bassepr.kpfu.ru/)) accessed on 15 December 2021.