A prerequisite for solving issues associated with surf zone variability, which affect human activity in coastal zones, is an accurate estimation of the effects of coastal protection methods. Therefore, performing frequent monitoring activities, especially when applying new nature-friendly coastal defense methods, is a major challenge. In this manuscript, we propose a pipeline for performing low-cost monitoring using RGB images, accessed by an unmanned aerial vehicle (UAV) and a four-level analysis architecture of an underwater object detection methodology. First, several color-based pre-processing activities were applied. Second, contrast-limited adaptive histogram equalization and the Hough transform methodology were used to automatically detect the underwater, circle-shaped elements of a hybrid coastal defense construction. An alternative pipeline was used to detect holes in the circle-shaped elements with an adaptive thresholding method; this pipeline was subsequently applied to the normalized images. Finally, the concatenation of the results from both the methods and the validation processes were performed. The results indicate that our automated monitoring tool works for RGB images captured by a low-cost consumer UAV. The experimental results showed that our pipeline achieved an average error of four pixels in the test set.

The human activities in the offshore oil and gas, renewable energy and construction industry require reliable data acquired by different types of hydrographic sensors: DGNSS (Differential Global Navigation Satellite System) positioning, attitude sensors, multibeam sonars, lidars or total stations installed on the offshore vessel, drones or platforms. Each component or sensor that produces information, unique to its position, will have a point that is considered as the reference point of that sensor. The accurate measurement of the offsets is vital to establish the mathematical relation between sensor and vessel common reference point in order to achieve sufficient accuracy of the survey data. If possible, the vessel will be put on a hard stand so that it can be very accurately measured using the standard land survey technique. However, due to the complex environment and sensors being mobilized when the vessel is in service, this may not be possible, and the offsets will have to be measured in sea dynamic conditions by means of a total station from a floating platform. This article presents the method of transformation by similarity with elements of affine transformation, called Q-ST (Quasi-Similarity Transformation). The Q-ST has been designed for measurements on such unstable substrates when it is not possible to level the total station (when the number of adjustment points is small (4–6 points)). Such situation occurs, among others, when measuring before the offshore duties or during the jack up or semi-submersible rig move. The presented calculation model is characterized by zero deviations at the adjustment points (at four common points). The transformation concerns the conversion of points between two orthogonal and inclined reference frames. The method enables the independent calculation of the scale factor, rotation matrix and system translation. Scaling is performed first in real space, and then both systems are shifted to the centroid, which is the center of gravity. The center of gravity is determined for the fit points that meet the criterion of stability of the orthogonal transformation. Then, the rotation matrix is computed, and a translation is performed from the computational (centroid) to real space. In the applied approach, the transformation parameters, scaling, rotation and translation, are determined independently, and the least squares method is applied independently at each stage of the calculations. The method has been verified in laboratory conditions as well as in real conditions. The results were compared to other known methods of coordinate transformation. The proposed approach is a development of the idea of transformation by similarity based on centroids.

Digital twin is an attractive technology for the representation of objects due to its ability to produce precise measurements and their geovisualisation. Of special interest is the application and fusion of various remote sensing techniques for shallow river and inland water areas, commonly measured using conventional surveying or multimodal photogrammetry. The construction of spatial digital twins of river areas requires the use of multi-platform and multi-sensor measurements to obtain reliable data of the river environment. Due to the high dynamics of river changes, the cost of measurements and the difficult-to-access measurement area, the mapping should be large-scale and simultaneous. To address these challenges, the authors performed an experiment using three measurement platforms (boat, plane, UAV) and multiple sensors to acquire both cloud and image spatial data, which were integrated temporally and spatially. The integration methods improved the accuracy of the resulting digital model by approximately 20 percent.

Modern technologies, such as airborne laser scanning (ALS) and advanced data analysis algorithms, allow for the efficient and safe use of resources to protect infrastructure from potential threats. This publication presents a study to identify trees that may fall on highways. The study used free measurement data from airborne laser scanning and wind speed and direction data from the Institute of Meteorology and Water Management in Poland. Two methods were used to determine the crown tops of trees: PyCrown and OPALS. The effect of wind direction on potential hazards was then analyzed. The OPALS method showed the best performance in terms of detecting trees, with an accuracy of 74%. The analysis showed that the most common winds clustered between 260° and 290°. Potential threats, i.e., trees that could fall on the road, were selected. As a result of the analysis, OPALS detected between 140 and 577 trees, depending on the chosen strategy. The presented research shows that combining ALS technology with advanced algorithms and wind data can be an effective tool for identifying potential hazards associated with falling trees on highways.

The research article describes a method of isometric transformation and determining an exterior orientation of a measurement instrument. The method is based on a designation of a “virtual” translation of two relative oblique orthogonal systems to a common, known in the both systems, point. The relative angle orientation of the systems does not change as each of the systems is moved along its axis. The next step is the designation of the three rotation angles (e.g. Tait-Bryan or Euler angles), transformation of the system convoluted at the calculated angles and moving the system to the initial position where the primary coordinate system was. This way eliminates movements of the systems from the calculations and makes it possible to calculate angles of mutual rotation angles of two orthogonal systems primarily involved in the movement. The research article covers laboratory calculations for simulated data. The accuracy of the results is 10-6 m (10-3 regarding the accuracy of the input data). This confi rmed the correctness of the assumed calculation method. In the following step the method was verifi ed under fi eld conditions, where the accuracy of the method raised to 0.003 m. The proposed method enabled to make the measurements with the oblique and uncentered instrument, e.g. total station instrument set over an unknown point. This is the reason why the method was named by the authors as Total Free Station - TFS. The method may be also used for isometric transformations for photogrammetric purposes.