Water body segmentation is an important tool for the hydrological monitoring of the Earth. With the rapid development of convolutional neural networks, semantic segmentation techniques have been used on remote sensing images to extract water bodies. However, some difficulties need to be overcome to achieve good results in water body segmentation, such as complex background, huge scale, water connectivity, and rough edges. In this study, a water body segmentation model (DUPnet) with dense connectivity and multi-scale pyramidal pools is proposed to rapidly and accurately extract water bodies from Gaofen satellite and Landsat 8 OLI (Operational Land Imager) images. The proposed method includes three parts: (1) a multi-scale spatial pyramid pooling module (MSPP) is introduced to combine shallow and deep features for small water bodies and to compensate for the feature loss caused by the sampling process; (2) dense blocks are used to extract more spatial features to DUPnet’s backbone, increasing feature propagation and reuse; (3) a regression loss function is proposed to train the network to deal with the unbalanced dataset caused by small water bodies. The experimental results show that the F1, MIoU, and FWIoU of DUPnet on the 2020 Gaofen dataset are 97.67%, 88.17%, and 93.52%, respectively, and on the Landsat River dataset, they are 96.52%, 84.72%, 91.77%, respectively.

Tectonic geomorphology is an important research area that uses multisource data to quantify the landscape response induced by the interaction between the tectonic uplift and climate changes. In this study, a comprehensive and quantitative analysis using bibliometric and scientometrics based on the research areas, countries, institutions, journals, authors, keywords, and citations is carried out, which provides an exhaustive history of tectonic geomorphology, and points out the hopspots and trends in the research area. A total of 2796 papers and 110,111 references from 1981 to 2021 are collected from Science Citation Index-Expanded (SCI-E) as the main data source. The results show that with the development of remote sensing, tectonic geomorphology, and the improvement of instruments and equipment, the amount of tectonic geomorphology analysis has been increasing. The journal Geomorphology is one of the most popular journals in this field. Through the co-occurrence network analysis, 12 clusters are identified in which the most popular research hotspot in tectonic geomorphology research is how to constrain the rates of active faulting using geomorphic indices. Through literature co-citation analysis, 13 research directions are extracted in which an important trend is to investigate the response of drainage divide migration to the fault slip rates. With the help of remote sensing data, physical attributes, and contextual knowledge, the reliability of measuring uplift rates under tectonic and climate changes has been increased. A future suggestion is to use multi-source heterogeneous data fusion to conduct quantitative analysis for tectonic geomorphology research.