  Lake extraction from remote sensing imagery is a complex challenge due to the
varied lake shapes and data noise. Current methods rely on multispectral image
datasets, making it challenging to learn lake features accurately from pixel
arrangements. This, in turn, affects model learning and the creation of
accurate segmentation masks. This paper introduces a prompt-based dataset
construction approach that provides approximate lake locations using point,
box, and mask prompts. We also propose a two-stage prompt enhancement
framework, LEPrompter, with prompt-based and prompt-free stages during
training. The prompt-based stage employs a prompt encoder to extract prior
information, integrating prompt tokens and image embedding through self- and
cross-attention in the prompt decoder. Prompts are deactivated to ensure
independence during inference, enabling automated lake extraction without
introducing additional parameters and GFlops. Extensive experiments showcase
performance improvements of our proposed approach compared to the previous
state-of-the-art method. The source code is available at
https://github.com/BastianChen/LEPrompter.
