Drainage network pattern recognition is a significant task with wide applications in
geographic information mining, map cartography, water resources management, and urban …

Drainage pattern recognition is crucial for geospatial understanding and hydrologic
modelling. Currently, drainage pattern recognition methods employ geometric measures of …

The spatial features and generalisation rules for river network generalisation are difficult to
directly quantify using indicators. To consider dimensional information hidden in river …

Drainage pattern recognition plays a crucial role in flood management, hydraulic
engineering site selection, and biodiversity maintenance. Although deep learning methods …

Drainage pattern recognition (DPR) is a classic and challenging problem in hydrographic
system analysis, topographical knowledge mining, and map generalization. An outstanding …

Existing research on automatic river network classification methods has difficulty
scientifically quantifying and determining feature threshold settings and evaluating weights …

Drainage pattern (DP) recognition is critical in hydrographic analysis, topography
identification, and drainage characteristic detection. The traditional method is based on rule …

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

Various geological factors shape drainage patterns. Identifying drainage patterns is a classic
problem in topographical knowledge mining and map generalization. Existing rule‐based …

Road networks are the skeleton of a city, its pattern recognition plays an important role in
urban landscape analysis, municipal planning, and traffic flow analysis. Road networks …