Automatic identification of cirques based on RetinaNet model and pseudo-color image fusion method.

Cirque landforms, which are distinctive features of cold regions, play a pivotal role in understanding environmental changes and climate research. Currently, machine learning-based automatic glacier recognition models have significantly improved recognition speed. However, these models rely solely on elevation data from Digital Elevation Model (DEM), neglecting other topographic factors that influence cirque identification, such as slope and aspect. This study integrates DEM, slope, and aspect data through pseudo-color fusion technology and utilizes the RetinaNet network to achieve automatic cirque recognition. The pseudo-color fusion image-based recognition model outperforms methods solely relying on DEM, achieving mAP (Intersection over Union: IoU @0.5) of 82.3 %. In practical applications, the pseudo-color fusion image-based recognition model demonstrates balanced performance in identifying different categories of cirques, particularly excelling in recognizing intricate terrains and irregularly shaped cirques. [ABSTRACT FROM AUTHOR]