We have created a deep-learning-based river network extraction model, called DeepRiver,
that learns the characteristics of rivers from synthetic data and generalizes them to natural …

Using existing methods to extract rivers, especially the small river from remote sensing
images, is liable to be interrupted. The combination of deep learning and multiple …

This study proposed a natural-rule-based-connection (NRBC) method to connect river
segments after water body detection from remotely sensed imagery. A complete river …

Drainage network extraction is essential for different research and applications. However,
traditional methods have low efficiency, low accuracy for flat regions, and difficulties in …

The extraction of rivers in cold and arid regions is of great significance for applications such
as ecological environment monitoring, agricultural planning, and disaster warning. However …

The extraction of rivers from remote sensing images is crucial for urban planning and water
resource utilization. To address the low accuracy of traditional methods for extracting rivers …

River-level estimation is a critical task required for the understanding of flood events and is
often complicated by the scarcity of available data. Recent studies have proposed to take …

River width can reveal the water extent and water flow on the earth's surface. Remote
sensing facilitates river width extraction at large scales in an automatic way. This letter …

The extraction of high-resolution geomorphic information from remote sensing images is a
key technology for supporting mountain river research. Extracting small rivers (width< 90 m) …

River segmentation of remote sensing images is of important research significance and
application value for environmental monitoring, disaster warning, and agricultural planning …