Image Assisted Total Stations (IATS) unify geodetic precision of total stations with areal coverage of images. The concept of using two IATS devices for high-resolution, long-range stereo survey of georisk areas has been investigated in the EU-FP7 project DE-MONTES (www.de-montes.eu). The paper presents the used methodology and compares the main features with other terrestrial geodetic geo-monitoring methods. The theoretically achievable accuracy of the measurement systemis derived and verified by ground truth data of a distant clay pit slope and simulated deformations. It is shown that the stereo IATS concept is able to obtain higher precision in the determination of 3D deformations than other systems of comparable sensor establishment effort.

Landslides represent a significant natural hazard with wide-reaching impacts. Addressing the challenge of accurately detecting and monitoring landslides, this research introduces a novel approach that combines feature tracking with histogram analysis for efficient outlier removal. Distinct from existing methods, our approach leverages advanced histogram techniques to significantly enhance the accuracy of landslide detection, setting a new standard in the field. Furthermore, when tested on three different data sets, this method demonstrated a notable reduction in outliers by approximately 15 to 25 percent of all displacement vectors, exemplifying its effectiveness. Key to our methodology is a refined feature tracking process utilizing terrestrial laser scanners, renowned for their precision and detail in capturing surface information. This enhanced feature tracking method allows for more accurate and reliable landslide monitoring, representing a significant advancement in geospatial analysis techniques.

In this work, we construct a Kd–Octree hybrid index structure to organize the point cloud and generate patch-based feature descriptors at its leaf nodes. We propose a simple yet effective convolutional neural network, termed KdO-Net, with Kd–Octree based descriptors as input for 3D pairwise point cloud matching. The classic pipeline of 3D point cloud registration involves two steps, viz., the point feature matching and the globally consistent refinement. We focus on the first step that can be further divided into three parts, viz., the key point detection, feature descriptor extraction, and pairwise-point correspondence estimation. In practical applications, the point feature matching is ambiguous and challenging owing to the low overlap of multiple scans, inconsistency of point density, and unstructured properties. To solve these issues, we propose the KdO-Net for 3D pairwise point feature matching and present a novel nearest neighbor searching strategy to address the computation problem. Thereafter, our method is evaluated with respect to an indoor BundleFusion benchmark, and generalized to a challenging outdoor ETH dataset. Further, we have extended our method over our complicated and low-overlapped TUM-lab dataset. The empirical results graphically demonstrate that our method achieves a superior precision and a comparable feature matching recall to the prior state-of-the-art deep learning-based methods, despite the overlap being less than 30 percent. Finally, we implement quantitative and qualitative ablated experiments and visualization interpretations for illustrating the insights and behavior of our network.

Water dams are an important infrastructure component for energy generation, water supply and flood control. Regular inspections of the structures for damage and deformation are necessary for safe operation and to ensure stability. In addition to the traditional concepts of geodetic network measurement, laser scan data can be used to deliver areal information on deformation. Within this topic, we aim at developing a method that identifies individual rocks of dam walls within the laser scans to introduce them as identical feature points for a rigorous deformation analysis. For this purpose, it is necessary to identify the solid stone surfaces on the water dams from scan data, and separated them from vegetation and joints. In this paper, we investigate method for identification of individual stones on gravity dams made of rubble stones. For the segmentation, the intensity values, RGB color information and local geometric structure from textured point clouds acquired with Terrestrial Laser Scanners are investigated. The classification should be robust against outer measurement conditions and provide sharp object boundaries. Our results show that – although many different methods are available – a reliable classification of single rubble stones is still a challenge task.