边坡地质灾害多源遥感图像智能预警方法及系统
By improving the Swing Transformer architecture and adaptive spatiotemporal attention mechanism, and combining it with a multimodal deep learning network, a multi-scale spatiotemporal feature coupling and disaster risk assessment closed-loop system was constructed. This system solves the problem of insufficient multi-scale feature capture and adaptability of multi-source remote sensing data fusion methods in slope geological disaster monitoring, and achieves highly accurate early warning.
CN121962935BActive Publication Date: 2026-07-17XIAN AERONAUTICAL UNIV
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- XIAN AERONAUTICAL UNIV
- Filing Date
- 2026-04-03
- Publication Date
- 2026-07-17
Smart Images
Figure CN121962935B_ABST
Abstract
本发明属于地质灾害监测与预警技术领域，公开了边坡地质灾害多源遥感图像智能预警方法及系统，方法包括：多源遥感数据自适应采集步骤，构建多源遥感数据立方体；多尺度时空特征耦合提取步骤，基于改进的Swin Transformer架构和自适应时空注意力机制生成时空耦合特征向量；灾害风险动态评估与反馈步骤，基于多模态深度学习网络计算灾害风险等级，根据风险评估结果动态调整特征融合权重，形成闭环反馈；智能预警决策与响应步骤，构建多级预警阈值体系触发预警并生成预警报告，本发明实现了多尺度时空特征自适应融合和闭环协同机制，显著提高了边坡地质灾害预警的准确性和智能化水平。
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