  Huge challenges exist for old landslide detection because their morphology
features have been partially or strongly transformed over a long time and have
little difference from their surrounding. Besides, small-sample problem also
restrict in-depth learning.
  In this paper, an iterative classification and semantic segmentation network
(ICSSN) is developed, which can greatly enhance both object-level and
pixel-level classification performance by iteratively upgrading the feature
extractor shared by two network. An object-level contrastive learning (OCL)
strategy is employed in the object classification sub-network featuring a
siamese network to realize the global features extraction, and a
sub-object-level contrastive learning (SOCL) paradigm is designed in the
semantic segmentation sub-network to efficiently extract salient features from
boundaries of landslides. Moreover, an iterative training strategy is
elaborated to fuse features in semantic space such that both object-level and
pixel-level classification performance are improved.
  The proposed ICSSN is evaluated on the real landslide data set, and the
experimental results show that ICSSN can greatly improve the classification and
segmentation accuracy of old landslide detection. For the semantic segmentation
task, compared to the baseline, the F1 score increases from 0.5054 to 0.5448,
the mIoU improves from 0.6405 to 0.6610, the landslide IoU improved from 0.3381
to 0.3743, and the object-level detection accuracy of old landslides is
enhanced from 0.55 to 0.9. For the object classification task, the F1 score
increases from 0.8846 to 0.9230, and the accuracy score is up from 0.8375 to
0.8875.
