A landslide susceptibility map (LSM) is of vital importance for risk recognition and prevention. In the last decade, statistical methods have gradually exerted their impact on mapping the landslide susceptibility to locate the high-risk places of landslide. However, due to the complexity of getting full access to the thematic information in large scenarios, most of these statistical methods generally suffer from overfitting, inadequate representative power, and the inability to transfer the learned representation to other places. To solve these challenges, this study designed an unsupervised representation learning module, which features independence, compactness, robustness, and transferability. Specifically, we first stack restricted Boltzmann machines and denoising autoencoder to unsupervised discover the underlying representations embedded in the thematic maps. Then, we applied the transferring strategy in an adversarial manner to generalize the learned representations to the sample-scarce area. Experimental results and analyses using data in different regions have revealed that the proposed method can be generalized well between different LSM scenarios. In terms of precision, it outperforms other methods by a large margin, e.g., by around 7% compared to multilayer perceptrons with the same configuration, and by 3%–4% to the state of art algorithm random forest. Besides, compared to other methods, the landslide susceptibility map that is predicted by the proposed method featuring smoothness and stableness seems more reliable, and is more according to some prior knowledge that, for example, distance to the drainage, slope, and stratum, should exert dominant effects on the occurrence of a landslide.

中文翻译：

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无监督特征学习提高滑坡敏感性表示的可迁移性


滑坡敏感性图（LSM）对于风险识别和预防至关重要。近十年来，统计方法逐渐在滑坡敏感性测绘中发挥作用，以定位滑坡高危地点。然而，由于在大场景下充分获取主题信息的复杂性，大多数这些统计方法普遍存在过度拟合、代表性能力不足以及无法将学习到的表示转移到其他地方的问题。为了解决这些挑战，本研究设计了一种无监督表示学习模块，该模块具有独立性、紧凑性、鲁棒性和可迁移性。具体来说，我们首先堆叠受限玻尔兹曼机和去噪自动编码器，以无监督地发现嵌入在专题图中的底层表示。然后，我们以对抗性方式应用转移策略，将学习到的表示推广到样本稀缺区域。使用不同地区数据的实验结果和分析表明，所提出的方法可以在不同的LSM场景之间很好地推广。在精度方面，它大大优于其他方法，例如，与具有相同配置的多层感知器相比，其精度提高了约 7%，与最先进的算法随机森林相比，精度提高了 3%–4%。此外，与其他方法相比，该方法预测的滑坡敏感性图具有平滑性和稳定性，似乎更可靠，并且更符合一些先验知识，例如距排水沟、边坡和地层的距离等。对滑坡的发生具有主导作用。