结合局部地理环境特征与空间上下文以增强滑坡易发性制图
Synergising Local Geo-Environmental Characteristics with Spatial Context for Enhancing Landslide Susceptibility Mapping
AI总结：
该研究提出LGSCF策略，整合至CNN架构构建9种模型，在台湾南投县2644km²区域的5332对滑坡与非滑坡样本上，使LSM的F1分数最高达87.09%、AUC达0.9472，提升了制图准确性。
AI中文摘要：
数据驱动方法被广泛应用于滑坡易发性制图（LSM），因为它们能有效建模滑坡与地理环境条件之间的复杂关系。现有数据驱动方法通常遵循两种数据表示类型：基于像素的模型仅关注特定滑坡的地理环境特征，却忽略其周边环境的影响；基于斑块的模型则纳入了周边空间上下文，但可能包含与目标滑坡位置空间相关性弱或无相关性的像素。为解决这一局限，本研究提出了局部地理与空间上下文融合（LGSCF）策略，该策略通过逐特征调制机制，将滑坡点的地理环境特征与其对应的空间上下文进行协同融合。我们将LGSCF策略整合到多个代表性卷积神经网络（CNN）架构中进行测试，构建了9种不同的基于LGSCF的模型。研究区域覆盖台湾南投县仁爱乡和信义乡，面积约2644平方公里，数据集包含5332个滑坡样本和数量相等的非滑坡样本。结果表明，基于LGSCF的模型始终优于其原始版本，F1分数最高达87.09%，AUC值最高达0.9472。此外，基于LGSCF模型生成的易发性图显示，已知滑坡更准确地集中在“极高”易发性区域，误分类更少。这些发现表明，我们的融合策略可显著提高滑坡易发性制图的准确性。
英文摘要：
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 primary 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 corresponding baselines, 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.
