Data

Regional landslide extraction is the basis of landslide hazard risk management. Acquiring landslide location and size information timely and effectively is crucial for disaster prevention and reduction. However, existing methods face challenges in accuratelyidentifyinglandslides due to inadequate learning of object features. Moreover, previous studies on the automated extraction of landslides have predominantly focused on those triggered by seismic events and precipitation, mining-induced landslides extractionremains comparatively limited. Here, we develop the MRSD-U-Net model, an enhanced deep learning network integrating multi-source remote sensing data and residual learning module.This model enhances the extraction of landslide features by deepening the network and capturing topographic, textural, and spectral information. The performance assessment show thatMRSD-U-Netachieves lower error rates, higher accuracy, and stronger generalization ability compared to other advanced deep learning models, thereby enabling a more accurate extraction of landslide boundaries. Furthermore, the landslide areas extracted by MRSD-U-Net are the most consistent with ground reference data. The research providesan effective method for the rapid and precise extraction of mining-induced landslides.

区域滑坡提取是滑坡灾害风险管理的基础。及时高效地获取滑坡的位置与规模信息，对于防灾减灾工作至关重要。然而，现有方法因对目标特征学习不足，难以精准识别滑坡。此外，以往关于滑坡自动提取的研究多集中于地震与降雨触发的滑坡，而采矿诱发型滑坡的提取研究相对较少。本研究构建了MRSD-U-Net模型，这是一种融合多源遥感数据（multi-source remote sensing data）与残差学习模块（residual learning module）的改进型深度学习网络。该模型通过加深网络结构、捕获地形、纹理与光谱信息，强化了滑坡特征的提取能力。性能评估结果表明，相较于其他先进深度学习模型，MRSD-U-Net的误差率更低、精度更高、泛化能力更强，可实现滑坡边界的精准提取。此外，MRSD-U-Net提取的滑坡面积与地面参考数据最为吻合。本研究为采矿诱发型滑坡的快速精准提取提供了有效方法。

搜集汇总

背景与挑战

背景概述

该数据集聚焦于矿区滑坡的自动提取，提出了MRSD-U-Net深度学习模型，通过整合多源遥感数据和残差学习模块，显著提升了滑坡边界提取的准确性和效率。研究为矿区滑坡的快速精确提取提供了有效方法。