Recognition of palaeo-glacial troughs with cirques on parts of Tibetan Plateau using multi-modal datasets with deep learning models

Recognition of landforms associated with past glaciation is crucial for understanding past ice dynamics and their relationship to climate. With the development of artificial intelligence technology, deep learning techniques have assisted in the automatic extraction of glacial landforms, but these methods still face problems of low precision and weak transferability. This study proposes a new method named geomorphology-attention DeeplabV3+ (GA-DeeplabV3+) model. This method adds spatial attention and channel attention modules based on the DeeplabV3+ network and utilizes a combination of multi-modal geographic data. Verification shows that the model proposed in this paper significantly enhances the recognition precision of glacial troughs with cirques compared to existing models, reaching a Mean Intersection over Union (MIoU) of 86.2% and a mean Pixel Accuracy (mPA) of 90.64% in the area of the Palaeo-Daocheng ice cap. In addition, validation experiments were conducted in the Peiku Gangri region and the Tenasserim mountains, achieving MIoU scores of 70.09% and 73.28% respectively. This accomplishment represents a vital stride towards automating the extraction of palaeo-glacial landforms, which holds great significance for analyzing the scale and evolution of ancient glaciers.