Landslide susceptibility mapping (LSM) is very important for hazard risk identification and prevention. Most of existing neural network models extract a pixel neighborhood feature or a pixel sequence feature of landslide factors on one side, which leads to the generalization ability of the network models difficultly, and had a low prediction accuracy in complex scenes. In this paper, a new unified network of information considering superimposed landslide factors sequence and pixel spatial neighbourhood is proposed for LSM. Different from the traditional prediction model framework, the landslide conditioning factors are merged into a unified network model in parallel with the pixel sequence features and pixel neighbourhood features. In the experiment, we take the proportion of landslide binary pixels as label data, which represents the landslide possibility in the neighbourhood. We propose a pixel sequence feature extraction algorithm based on a gated recurrent unit (GRU) network and a pixel neighbourhood feature extraction algorithm based on a multi-scale convolution neural network (MSCNN). In this study, the landslide conditioning factors were analysed by multicollinearity analysis and the frequency ratio (FR) method. The performance of the modes was evaluated by statistical indexes and the correlation analysis. The LSM results were verified by google earth images and field investigation. Our research shows that the proposed model can greatly improve the accuracy of LSM compared with the individual GRU and MSCNN, especially, the proposed model had 6.1% more improvement than the GRU model in terms of the area under curve (AUC). Therefore, we suggest that the proposed model is a suitable technology for use in early identification and landslide prediction.

滑坡敏感性绘图（LSM）对于灾害风险识别和预防非常重要。现有的神经网络模型大多提取一侧滑坡因素的像素邻域特征或像素序列特征，导致网络模型的泛化能力较难，在复杂场景中预测精度较低。在本文中，为LSM 提出了一种考虑叠加滑坡因素序列和像素空间邻域的新的统一信息网络。不同于传统的预测模型框架，滑坡条件因子与像素序列特征和像素邻域特征并行合并为一个统一的网络模型。在实验中，我们以滑坡二进制像素的比例作为标签数据，这代表了附近发生山体滑坡的可能性。我们提出了一种基于门控循环单元（GRU）网络的像素序列特征提取算法和基于多尺度卷积神经网络（MSCNN）的像素邻域特征提取算法。本研究采用多重共线性分析和频率比（FR）法对滑坡条件因子进行分析。通过统计指标和相关性分析来评估模式的性能。LSM 结果通过谷歌地球图像和实地调查验证。我们的研究表明，与单独的 GRU 和 MSCNN 相比，所提出的模型可以大大提高 LSM 的准确性，尤其是在曲线下面积 (AUC) 方面，所提出的模型比 GRU 模型提高了 6.1%。所以，