Key points are not available for this paper at this time.
Accurate mapping of loess waterworn gully (LWG) is essential to further study gully erosion and geomorphological evolution for the Chinese Loess Plateau (CLP). Due to the vertical joint and collapsibility of loess, LWGs have the characteristics of zigzag and unique slope abruptness under synthetic action of hydraulic force and gravity. This forces existing LWG mapping methods to either focus on the improvement of mapping accuracy or center on the increase of mapping efficiency. However, simultaneously achieving accurate and efficient mapping of LWG is still in its infancy under complex topographic conditions. Here, we proposed a method that innovatively integrates the loess slope abruptness feature into an improved deep learning semantic segmentation framework for LWG mapping using 0.6 m Google imagery and 5 m DEM data. We selected four study areas representing typical loess landforms to test the performance of our method. The proposed method can achieve satisfactory mapping results, with the F1 score, mean Intersection-over-Union (mIoU), and overall accuracy of 90.5%, 85.3%, and 92.3%, respectively. In addition, the proposed model also showed significant accuracy improvement by inputting additional topographic information (especially the slope of slope). Compared with existing algorithms (Random forests, original DeepLabV3+, and Unet), the proposed approach in this study achieved a better accuracy-efficiency trade-off. Overall, the method can ensure high accuracy and efficiency of the LWG mapping for different loess landform types and can be extended to study various loess gully mapping and water and soil conservation.
Chen et al. (Thu,) studied this question.