Point clouds are currently used for a variety of applications, such as detection tasks in medical and geological domains. Intelligent analysis of point clouds is considered a highly computationally demanding and challenging task, especially the segmentation task among the points. Although numerous deep learning models have recently been proposed to segment point cloud data, there is no clear instruction of which exactly neural network to utilize and then incorporate into a system dealing with point cloud segmentation analysis. Besides, the majority of the developed models emphasize more on the accuracy rather than the efficiency, in order to achieve great results. Consequently, the training, validation and testing phases of the models require a great number of processing hours and a huge amount of memory. These high computational requirements are commonly difficult to deal with for many users. In this article, we analyse five state-of-the-art deep learning models for part segmentation task and give meaningful insights into the utilization of each one. We advance guidelines based on different properties, considering both learning-related metrics, such as accuracy, and system-related metrics, such as run time and memory footprint. We further propose and analyse generalized performance metrics, which facilitate the model evaluation phase in segmentation tasks allowing users to select the most appropriate approach for their context in terms of accuracy and efficiency.
Rock slope monitoring using 3D point cloud data allows the creation of rockfall inventories, provided that an efficient methodology is available to quantify the activity. However, monitoring with high temporal and spatial resolution entails the processing of a great volume of data, which can become a problem for the processing system. The standard methodology for monitoring includes the steps of data capture, point cloud alignment, the measure of differences, clustering differences, and identification of rockfalls. In this article, we propose a new methodology adapted from existing algorithms (multiscale model to model cloud comparison and density-based spatial clustering of applications with noise algorithm) and machine learning techniques to facilitate the identification of rockfalls from compared temporary 3D point clouds, possibly the step with most user interpretation. Point clouds are processed to generate 33 new features related to the rock cliff differences, predominant differences, or orientation for classification with 11 machine learning models, combined with 2 undersampling and 13 oversampling methods. The proposed methodology is divided into two software packages: point cloud monitoring and cluster classification. The prediction model applied in two study cases in the Montserrat conglomeratic massif (Barcelona, Spain) reveal that a reduction of 98% in the initial number of clusters is sufficient to identify the totality of rockfalls in the first case study. The second case study requires a 96% reduction to identify 90% of the rockfalls, suggesting that the homogeneity of the rockfall characteristics is a key factor for the correct prediction of the machine learning models.
Rockfall detection is a crucial procedure in the field of geology, which helps to reduce the associated risks. Currently, geologists identify rockfall events almost manually utilizing point cloud and imagery data obtained from different caption devices such as Terrestrial Laser Scanner (TLS) or digital cameras. Multitemporal comparison of the point clouds obtained with these techniques requires a tedious visual inspection to identify rockfall events which implies inaccuracies that depend on several factors such as human expertize and the sensibility of the sensors. This paper addresses this issue and provides an intelligent framework for rockfall event detection for any individual working in the intersection of the geology domain and decision support systems. The development of such an analysis framework presents major research challenges and justifies exhaustive experimental analysis. In particular, we propose an intelligent system that utilizes multiple machine learning algorithms to detect rockfall clusters of point cloud data. Due to the extremely imbalanced nature of the problem, a plethora of state‐of‐the‐art resampling techniques accompanied by multiple models and feature selection procedures are being investigated. Various machine learning pipeline combinations have been examined and benchmarked applying well‐known metrics to be incorporated into our system. Specifically, we developed machine learning techniques and applied them to analyze point cloud data extracted from TLS in two distinct case studies, involving different geological contexts: the basaltic cliff of Castellfollit de la Roca and the conglomerate Montserrat Massif, both located in Spain. Our experimental results indicate that some of the above‐mentioned machine learning pipelines can be utilized to detect rockfall incidents on mountain walls, with experimentally validated accuracy.
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