Rockfalls are critical landslide phenomena affecting human activities, with risk assessment based on hazard evaluation and potential impacts on exposed elements. Traditional methods for estimating unstable rock block volumes require direct measures often in hard-to-reach areas, hazardous, with time consuming approaches. This study introduces a semi-automatic method for estimating the most probable volume of the unstable blocks using open-source software (CloudCompare) to process 3D Point Cloud (PC) data obtained via Terrestrial Laser Scanning (TLS). The application area is a rock slope in a coastal sector of the northern Sicily (Italy) affected by frequent rockfalls phenomena. Both traditional field surveys and TLS were employed to characterize discontinuities and perform kinematic analyses. Volumes of already fallen blocks were directly measured, while unstable blocks were identified and volumetrically assessed using the PC-based procedure. Statistical analysis revealed that both created datasets conform to lognormal distributions; direct measurements show a better fit due to a larger sample size. Moreover, direct and indirect approaches were applied for recognition of main discontinuity sets influencing block detachment through planar sliding, toppling, and wedge failure. The proposed method offers a safer, more efficient alternative for rock mass characterization. Integration of traditional and remote sensing techniques facilitates accurate hazard evaluation, enhancing risk reduction strategies in vulnerable areas.

中文翻译：

---

从高分辨率 3D 点云中半自动提取岩石块体积，以增强落石危害分析


落石是影响人类活动的关键滑坡现象，风险评估基于危害评估和对暴露元素的潜在影响。估算不稳定岩石块体积的传统方法需要直接在难以到达的区域进行测量，这些区域很危险，而且方法耗时。本研究介绍了一种半自动方法，使用开源软件 （CloudCompare） 估计不稳定块的最可能体积，以处理通过地面激光扫描 （TLS） 获得的 3D 点云 （PC） 数据。应用领域是西西里岛北部（意大利）沿海地区的一个岩石边坡，受频繁落石现象的影响。传统的现场调查和 TLS 都用于表征不连续性并进行运动学分析。直接测量已经掉落的块的体积，而使用基于 PC 的程序识别不稳定的块并进行体积评估。统计分析表明，创建的两个数据集都符合对数正态分布;由于样本量较大，直接测量显示拟合度更高。此外，直接和间接方法用于识别通过平面滑动、倾倒和楔形破坏影响块体分离的主要不连续集。所提出的方法为岩体表征提供了一种更安全、更高效的替代方案。传统技术和遥感技术的整合有助于准确的灾害评估，加强脆弱地区的风险降低策略。