自然资源快速执法模型优化方法研究
Research on Optimization of Rapid Enforcement Models for Natural Resource Management

本文针对自然资源执法效率低的问题，提出基于深度学习的快速执法模型优化方法。构建高质量多时相遥感变化样本库，优化样本结构与多层级标签体系。在商汤预训练模型基础上，引入CNN？ViT融合、特征对齐及边界–区域复合损失，实现高精度双时相变化检测。实验与实际应用表明，模型识别精度高达95.96%，检测效率较人工提升约10倍，实现执法由“被动核查”向“主动发现”转变。
This paper addresses the low efficiency of natural resource law enforcement and proposes a deep learning-based rapid enforcement model optimization method. A high-quality multi-temporal remote sensing change detection sample library is constructed, with optimized sample structure and multi-level label system. Based on Sense Time’s pre-trained model, CNN？ViT fusion, feature alignment, and boundary-region composite loss are introduced to achieve high-precision bi-temporal change detection. Experiments and practical applications show that the model achieves a recognition accuracy of 95.96% and a detection efficiency approximately 10 times higher than manual interpretation, enabling a shift from “passive inspection” to “active discovery” in law enforcement.