landslide

The dataset consists of satellite optical images, landslide boundary shapefiles, and digital elevation models. It includes 770 landslide samples, comprising rockfalls, rockslides, and some debris landslides, along with 2003 negative samples covering various backgrounds. These samples were cropped from TripleSat satellite images taken between May and August 2018, with an image resolution of 0.8 meters.The sample image size ranges from 128 × 128 to 1024 × 1024 pixels, and each image has three spectral channels (R, G, and B). For the DEM, the spatial resolution is 0.8 meters. Figure 2 illustrates the data images for both landslide and non-landslide cases. To balance the sample sizes of landslide and non-landslide images, data augmentation techniques are employed in the experiment to increase the number of landslide samples. In the experiments, the dataset is divided into 80% for training and 20% for testing. Within the training set, 10% is allocated for validation purposes.本数据集包含卫星光学影像、滑坡边界shapefile文件与数字高程模型（Digital Elevation Model, DEM）。数据集共收录770个滑坡样本，涵盖岩石崩塌、岩质滑坡以及部分碎屑滑坡，同时搭配2003个覆盖多样背景的负样本。上述样本均裁剪自2018年5月至8月期间获取的TripleSat卫星影像，影像空间分辨率为0.8米。样本影像尺寸介于128×128至1024×1024像素之间，单幅影像包含红（R）、绿（G）、蓝（B）三个光谱通道。数字高程模型的空间分辨率同样为0.8米。图2展示了滑坡样本与非滑坡样本的示例影像。为平衡滑坡与非滑坡样本的数量规模，实验中采用数据增强技术以扩充滑坡样本量。实验阶段将数据集按8:2的比例划分为训练集与测试集，其中训练集内再分出10%作为验证集。

搜集汇总

背景与挑战

背景概述

该数据集用于滑坡检测研究，包含2018年TripleSat卫星获取的高分辨率光学图像（0.8米）、数字高程模型（DEM）及滑坡边界标签，共2773个样本（770个正例和2003个负例）。图像尺寸多样，通过数据增强平衡类别，并按80%训练、10%验证、20%测试划分，适用于基于深度学习的遥感图像分割任务。