TeLIP: Terrain-Based Landslide Initiation Position Model

This dataset is designed to support the training and evaluation of the <b>TeLIP (Terrain-based Landslide Initiation Position Model)</b>, a deep learning model aimed at predicting landslide initiation positions based on topographic and geologic features of hillslopes. The dataset includes the following three types of input data, which are essential for the model's operation:<b>Geological Data</b>: Contains various predictors such as relative height, elevation, slope, soil thickness, profile curvature, plan curvature, TWI (Topographic Wetness Index), and position index. These features are used to capture the spatial characteristics of the terrain.<b>Landslide Position Data</b>: The labels in this dataset indicate whether a given position along the slope is the initiation point of a landslide. These binary classification labels are essential for training the model to predict the landslide initiation positions.<b>Slope Length Data</b>: Provides the total length of each slope segment, which is necessary for evaluating prediction errors in terms of the distance between predicted and true landslide positions.Users can create their corresponding training datasets based on the following data format according to their landslide research objectives.The source code of the TeLIP framework is available on GitHub at https://github.com/Wujunyu0305/TeLIP

本数据集旨在支撑TeLIP（Terrain-based Landslide Initiation Position Model，基于地形的滑坡起始位置模型）的训练与评估，该深度学习模型旨在依据坡面的地形与地质特征预测滑坡起始位置。数据集包含三类模型运行必需的输入数据：
1. **地质数据（Geological Data）**：涵盖相对高差、高程、坡度、土层厚度、剖面曲率、平面曲率、地形湿度指数（Topographic Wetness Index, TWI）以及位置指数等多种预测因子，用于捕捉地形的空间分布特征。
2. **滑坡位置数据（Landslide Position Data）**：本数据集的标签用于指示坡面给定位置是否为滑坡起始点，这类二分类标签是训练模型预测滑坡起始位置的核心要素。
3. **坡长数据（Slope Length Data）**：提供各坡面段的总长度，可用于基于预测滑坡位置与真实滑坡位置之间的距离评估预测误差。
用户可根据自身的滑坡研究目标，按照下述数据格式构建对应的训练数据集。TeLIP框架的源代码可在GitHub获取，链接为https://github.com/Wujunyu0305/TeLIP