Photogrammetry is increasingly being used to characterize rock slope hazards in mountainous environments. With growth in the amount of point cloud data being collected, there is a need for algorithmic and rapid point cloud interpretation methods to prepare the data for engineering analysis. However, there is a lack of semantic segmentation literature focused on geological datasets and the potential challenges they pose in a practical setting, such as non-ideal data collection parameters, variable lighting conditions, and label noise. This study presents smartphone and UAV photogrammetry datasets including multiple slope morphologies and lighting conditions, manually labels them to create a training database, and classifies points into five geologically relevant categories using Random Forest. Datasets were collected at multiple times of day and seasons to include varying proportions of shadowed areas, examples of clear and overcast skies, and snow. We compared 12 different point cloud feature sets from previous studies, including combinations of geometric, slope, absolute color, and texture features. In addition, we propose multi-scale color standard deviation and the Grey-Level Co-occurrence Matrix as potentially useful descriptors of texture. Accuracy results generally indicate that feature sets containing absolute color features are highly sensitive to changes in lighting conditions and they are not able to discern between various geological materials (soil, talus, rock). In contrast, our novel feature sets with only geometry, slope, and texture were significantly less influenced by lighting and were on average the highest overall accuracy of any feature set tested, with an average overall accuracy of around 80%.

摄影测量法越来越多地用于表征山区环境中的岩石边坡危害。随着收集的点云数据量的增长，需要算法和快速点云解释方法来准备用于工程分析的数据。但是，缺乏针对地理数据集的语义分割文献，以及它们在实际环境中带来的潜在挑战，例如非理想的数据收集参数，可变的光照条件和标签噪声。这项研究提出了智能手机和无人机摄影测量数据集，包括多种坡度形态和光照条件，对其进行手动标记以创建训练数据库，并使用随机森林将点分为五个与地质相关的类别。在一天和每个季节的多个时间收集数据集，以包括不同比例的阴影区域，晴朗和阴暗的天空以及下雪的示例。我们比较了先前研究中的12种不同的点云特征集，包括几何，坡度，绝对颜色和纹理特征的组合。另外，我们提出多尺度颜色标准差和灰度共现矩阵作为纹理的潜在有用描述符。准确性结果通常表明，包含绝对颜色特征的特征集对光照条件的变化高度敏感，并且无法区分各种地质材料（土壤，距骨，岩石）。相比之下，我们新颖的功能集仅包含几何图形，坡度，