On the 3D Reconstruction of Coastal Structures by Unmanned Aerial Systems with Onboard Global Navigation Satellite System and Real-Time Kinematics and Terrestrial Laser Scanning

Abstract

A wide variety of hard structures protect coastal activities and communities from the action of tides and waves worldwide. It is fundamental to monitor the integrity of coastal structures, as interventions and repairs may be needed in case of damages. This work compares the effectiveness of an Unmanned Aerial System (UAS) and a Terrestrial Laser Scanner (TLS) to reproduce the 3D geometry of a rocky groin. The Structure-from-Motion (SfM) photogrammetry technique applied on drone images generated a 3D point cloud and a Digital Surface Model (DSM) without data gaps. Even though the TLS returned a 3D point cloud four times denser than the drone one, the TLS returned a DSM which was not representing about 16% of the groin (data gaps). This was due to the occlusions encountered by the low-lying scans determined by the displaced rocks composing the groin. Given also that the survey by UAS was about eight time faster than the TLS, the SFM-MV applied on UAS images was the most suitable technique to reconstruct the rocky groin. The UAS remote sensing technique can be considered a valid alternative to monitor all types of coastal structures, to improve the inspection of likely damages, and to support coastal structure management.

Structural health monitoring of inland navigation structures and ports: a review on developments and challenges

“…A TLS can generate a four times denser 3D point cloud than a drone mapping, but it is three times slower in acquiring data than a drone. 214 A UAV mounted with a LiDAR can generate a high surface density model comparable to a ground TLS, but it can be a costly option. 215 The armor layer porosity and packing density of rubble mound groynes having regular size units (shape factor of 60%–80%) can be monitored efficiently utilizing UAV-based remote sensing.…”

Filtering Green Vegetation Out from Colored Point Clouds of Rocky Terrains Based on Various Vegetation Indices: Comparison of Simple Statistical Methods, Support Vector Machine, and Neural Network

“…It is worth noting that although mass data collection methods are currently very popular in many fields of research involving complex terrain [26], such as coastal monitoring [27][28][29], volcano exploration [30], underground research [31,32], tree detection [33,34], the evolution of rock glaciers [35], monitoring of rock masses [36][37][38], or their geological analysis [39], vegetation filtering in such cases is typically handled by human operators as the performance of automated algorithms proposed for this purpose, so far, is generally poor as confirmed by Blanco et al [6]. Specifically, they usually have problems when encountering highly rugged and/or sloped terrain [15].…”

DSM Generation from Multi-View High-Resolution Satellite Images Based on the Photometric Mesh Refinement Method

“…A digital surface model (DSM) is a series of point clouds or a mesh model with threedimensional coordinates that represent the undulations of the surface (including artificial buildings, vegetation, etc.). It is an important data source for making true digital orthophoto maps (TDOM), extracting digital elevation models (DEMs), updating geographic information databases, and extracting buildings and generating contour lines in the process of producing map products [1][2][3][4]. Most of the current methods for generating DSMs are based on photogrammetry and involve acquiring the corresponding points in airborne images before then relying on image-matching techniques to complete the generation of the DSM.…”