As a harzard disaster, landslide often brings tremendous losses to humanity, so it's necessary to achieve reliable detection of landslide. However, the problems of visual blur and small-sized dataset cause great challenges for old landslide detection task when using remote sensing data. To reliably extract semantic features, a hyper-pixel-wise contrastive learning augmented segmentation network (HPCL-Net) is proposed, which augments the local salient feature extraction from the boundaries of landslides through HPCL and fuses the heterogeneous infromation in the semantic space from High-Resolution Remote Sensing Images and Digital Elevation Model Data data. For full utilization of the precious samples, a global hyper-pixel-wise sample pair queues-based contrastive learning method, which includes the construction of global queues that store hyperpixel-wise samples and the updating scheme of a momentum encoder, is developed, reliably enhancing the extraction ability of semantic features. The proposed HPCL-Net is evaluated on a Loess Plateau old landslide dataset and experiment results show that the model greatly improves the reliablity of old landslide detection compared to the previous old landslide segmentation model, where mIoU metric is increased from 0.620 to 0.651, Landslide IoU metric is increased from 0.334 to 0.394 and F1score metric is increased from 0.501 to 0.565.