【摘要】 基于AI大模型的遥感影像地类要素识别与提取已成为解决其在能源电力领域深化应用的关键技术，而模型训练依赖于海量样本数据。针对样本库建立时标注工作量大、自动化程度低，质量不高的问题，通过研究影响样本制作的因素及在推理结果中映射形式，提出根据目标类别针对性地设计样本库架构，并采用基于点、线提示的SAM模型实现高精度半自动化样本快速标注，结合小样本学习模型验证样本库构建效果。结果表明，利用所构建的样本库对模型训练后，各类要素推理边缘准确且精度相当，说明样本库分布合理、结构较好、标注精度较高。该方法能够提高样本智能化制作、减少人工工作量、保证样本库质量，为遥感影像AI模型样本库建立提供参考思路。更多还原

【Abstract】 The recognition and extraction of land features in remote sensing images based on AI big models has become a key technology to deepen its application in the field of energy and power,and model training relies on massive sample data.This article addresses the issues of high annotation workload,low automation level,and low quality when establishing a sample library.By studying the factors that affect sample production and the mapping form in inference results,a sample library architecture is proposed that is targeted according to the target category.A SAM model based on point and line representation is used to achieve high-precision semi-automatic sample rapid annotation,and the construction effect of the sample library is verified by combining a small sample learning model.The results indicate that using the constructed sample library for model training,the inference edges of various elements are accurate and have comparable accuracy,indicating that the sample library distribution is reasonable,the structure is good,and the annotation accuracy is high.This method can improve the intelligent production of samples,reduce manual workload,and ensure the quality of sample libraries,providing reference ideas for the establishment of remote sensing image AI model sample libraries.更多还原