  Land-cover mapping is one of the vital applications in Earth observation,
aiming at classifying each pixel's land-cover type of remote-sensing images. As
natural and human activities change the landscape, the land-cover map needs to
be rapidly updated. However, discovering newly appeared land-cover types in
existing classification systems is still a non-trivial task hindered by various
scales of complex land objects and insufficient labeled data over a wide-span
geographic area. In this paper, we propose a generalized few-shot
segmentation-based framework, named SegLand, to update novel classes in
high-resolution land-cover mapping. Specifically, the proposed framework is
designed in three parts: (a) Data pre-processing: the base training set and the
few-shot support sets of novel classes are analyzed and augmented; (b) Hybrid
segmentation structure; Multiple base learners and a modified Projection onto
Orthogonal Prototypes (POP) network are combined to enhance the base-class
recognition and to dig novel classes from insufficient labels data; (c)
Ultimate fusion: the semantic segmentation results of the base learners and POP
network are reasonably fused. The proposed framework has won first place in the
leaderboard of the OpenEarthMap Land Cover Mapping Few-Shot Challenge.
Experiments demonstrate the superiority of the framework for automatically
updating novel land-cover classes with limited labeled data.
