  As a natural disaster, landslide often brings tremendous losses to human
lives, so it urgently demands reliable detection of landslide risks. When
detecting old landslides that present important information for landslide risk
warning, problems such as visual blur and small-sized dataset cause great
challenges when using remote sensing data. To extract accurate semantic
features, a hyper-pixel-wise contrastive learning augmented segmentation
network (HPCL-Net) is proposed, which augments the local salient feature
extraction from boundaries of landslides through HPCL-Net and fuses
heterogeneous infromation in the semantic space from high-resolution remote
sensing images and digital elevation model data. For full utilization of
precious samples, a global hyper-pixel-wise sample pair queues-based
contrastive learning method is developed, which includes the construction of
global queues that store hyper-pixel-wise samples and the updating scheme of a
momentum encoder, reliably enhancing the extraction ability of semantic
features. The proposed HPCL-Net is evaluated on the Loess Plateau old landslide
dataset and experimental results verify that the proposed HPCL-Net greatly
outperforms existing models, where the mIoU is increased from 0.620 to 0.651,
the Landslide IoU is improved from 0.334 to 0.394 and the F1score is enhanced
from 0.501 to 0.565.
