Using Global Land Cover Product as Prompt for Cropland Mapping via
  Visual Foundation Model
Chao Tao Aoran Hu Rong Xiao Haifeng Li Yuze Wang
Data-driven deep learning methods have shown great potential in cropland
mapping. However, due to multiple factors such as attributes of cropland
(topography, climate, crop type) and imaging conditions (viewing angle,
illumination, scale), croplands under different scenes demonstrate a great
domain gap. This makes it difficult for models trained in the specific scenes
to directly generalize to other scenes. A common way to handle this problem is
through the "Pretrain+Fine-tuning" paradigm. Unfortunately, considering the
variety of features of cropland that are affected by multiple factors, it is
hardly to handle the complex domain gap between pre-trained data and target
data using only sparse fine-tuned samples as general constraints. Moreover, as
the number of model parameters grows, fine-tuning is no longer an easy and
low-cost task. With the emergence of prompt learning via visual foundation
models, the "Pretrain+Prompting" paradigm redesigns the optimization target by
introducing individual prompts for each single sample. This simplifies the
domain adaption from generic to specific scenes during model reasoning
processes. Therefore, we introduce the "Pretrain+Prompting" paradigm to
interpreting cropland scenes and design the auto-prompting (APT) method based
on freely available global land cover product. It can achieve a fine-grained
adaptation process from generic scenes to specialized cropland scenes without
introducing additional label costs. To our best knowledge, this work pioneers
the exploration of the domain adaption problems for cropland mapping under
prompt learning perspectives. Our experiments using two sub-meter cropland
datasets from southern and northern China demonstrated that the proposed method
via visual foundation models outperforms traditional supervised learning and
fine-tuning approaches in the field of remote sensing.