Photogrammetry is one of the most reliable techniques to generate high-resolution topographic data and it is key to territorial mapping and change detection analysis of landforms in hydro-geomorphological high-risk areas. Specifically, the Structure from Motion (SfM) is an emerging topographic survey technique that addresses the problem of determining the 3D position of image descriptors to estimate three-dimensional structures. Thanks to the potential of SfM algorithm and the development of Unmanned Aerial Vehicles (UAVs) that allow the on-demand acquisition of high-resolution aerial images, it is possible to survey extended areas of the Earth surface and monitor active phenomena through multi-temporal surveys. However, the ability to detect remote and wide areas with a very high-resolution is countered by the need to capture large datasets which can limit the photogrammetric process, due to the need for high-performance hardware. This paper presents a photogrammetric workflow based on Free and Open-Source Software (FOSS), which is able to return different outputs and to manage a large amount of data in reasonable time, through the distribution of the most computationally expensive steps on computing clusters hosted by the ReCaS-Bari data center for scientific research. The results are given in terms of performance evaluations based on different computing configurations of the clusters and setups of the steps of the workflow. The HTC cluster test with a parallel SSH approach involved an important reduction of several hours in the processing time of thousands UAV images, especially compared to classic photogrammetric process on a single workstation with commercial software.

A parallel test, aimed to validate the performance of a single sever of the new HPC cluster, involved really good results halving the processing time with respect to the HTC cluster test.

摄影测量是生成高分辨率地形数据的最可靠技术之一，是水文地貌高风险地区地形测绘和地貌变化检测分析的关键。具体来说，运动结构 (SfM) 是一种新兴的地形测量技术，它解决了确定图像描述符的 3D 位置以估计 3D 结构的问题。由于 SfM 算法的潜力和无人驾驶飞行器 (UAV) 的发展，允许按需获取高分辨率航空图像，因此可以通过多时相测量地球表面的扩展区域并监测活动现象调查。然而，由于需要高性能硬件，因此需要捕获可能限制摄影测量过程的大型数据集，从而抵消了以非常高的分辨率检测偏远和广阔区域的能力。本文提出了一种基于自由和开源软件 (FOSS) 的摄影测量工作流，该工作流能够返回不同的输出并在合理的时间内管理大量数据，通过在托管的计算集群上分布计算成本最高的步骤由 ReCaS-Bari 数据中心用于科学研究。根据集群的不同计算配置和工作流步骤的设置，在性能评估方面给出了结果。

并行测试旨在验证新 HPC 集群的单个服务器的性能，结果非常好，与 HTC 集群测试相比，处理时间减半。