融合局部地质特征与空间上下文，提升滑坡易发性预测精度
Synergising Local Geo-Environmental Characteristics with Spatial Context for Enhancing Landslide Susceptibility Mapping
- 通过特征调制机制融合滑坡点的局部特征与周边环境
- 模型F1最高达87.09%，AUC达0.9472，优于原始模型
- 适合地质灾害评估、地理信息建模等应用领域
数据驱动方法广泛用于滑坡易发性制图（LSM），可有效建模滑坡与地质环境条件间的复杂关系。现有方法主要分为两类：基于像素的模型仅关注目标滑坡点的地质特征，忽略周围环境影响；基于块的模型虽包含空间上下文，但可能引入无关像素。为此，本文提出局部地质特征与空间上下文融合（LGSCF）策略，通过特征级调制机制融合滑坡点的局部特征与其对应的空间上下文。将LGSCF集成至多个典型卷积神经网络（CNN）架构中，构建九种基于LGSCF的模型。研究区域覆盖台湾南投县集集与信义两乡镇，总面积约2644 km²，数据集包含5332个滑坡样本与同等数量的非滑坡样本。结果表明，基于LGSCF的模型持续优于原模型，F1得分最高达87.09%，AUC值最高达0.9472。且其生成的易发性地图中，已知滑坡更集中于“极高”易发区，误判更少。结果表明该融合策略显著提升了滑坡易发性制图精度。
原文摘要 · Abstract (English)
Data-driven methods are widely used in landslide susceptibility mapping (LSM) because they can effectively model the complex relationships between landslides and geo-environmental conditions. Existing data-driven approaches generally follow two types of data representations. Pixel-based models focus solely on the geo-environmental characteristics of a specific landslide but neglect the influence of its surrounding environment. Patch-based models incorporate surrounding spatial context but may include pixels with weak or no spatial relevance to the target landslide location. To address this limitation, this study proposes a Local-Geo and Spatial Context Fusion (LGSCF) strategy, which synergises the geo-environmental characteristics of landslide points with their corresponding spatial context through a feature-wise modulation mechanism. We tested the LGSCF strategy by integrating it into several representative convolutional neural network (CNN) architectures, creating nine different LGSCF-based models. The study area covers approximately 2644 km2 across Jenai and Sinyi Townships in Nantou County, Taiwan, and the dataset comprises 5332 landslide samples and an equal number of non-landslide samples. The results show that LGSCF-based models consistently outperform their original versions, achieving F1-scores up to 87.09% and AUC values up to 0.9472. Furthermore, the susceptibility maps produced by LGSCF-based models show that known landslides are more accurately concentrated in "very high" susceptibility zones with fewer misclassifications. These findings demonstrate that our fusion strategy can significantly improve the accuracy of landslide susceptibility mapping.
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