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Topographical changes in riverine floodplains need to be measured for assessing geomorphological dynamics and protecting floodplain areas, although topographic evaluation in disaster-prone floodplain in South Asia has been limited due to the lack of multitemporal, high-definition digital elevation models (DEMs) derived from modern techniques including airborne laser scanning, structure-from-motion (SfM) photogrammetry accompanied with Unmanned Aerial Vehicle (UAV, often referred as drone), and field-based mapping approaches. Here we conducted a preliminary study at two locations of Teesta River (Brahmaputra's tributary) in Bangladesh using the UAV-SfM techniques and generated high-resolution DEMs. The selected locations represent dynamic changes of sediment and water on the floodplain over the years, and the UAV-SfM approach can be an effective method for monitoring those changes, but an archive of the past data has been unavailable. Here we evaluated the topographic changes by comparing the UAV-SfM-derived DEMs of 2022 with global DEM products (NASADEM of 1999), which are often the only available choice of DEMs in this river floodplain. The elevation differences of these two sets of DEMs were in the range of −5.23 to −84.66 m, and volumetric changes of −4.11 ± 0.15 to −86.25 ± 0.20 million m3, likely dominated by erosional processes towards the left side bank where the elevation errors are supposed to be several meters for UAV-DEM and ca. 5–12 m for NASADEM. Although it is not easy to accurately evaluate the absolute values of the changes, these changes may be associated with the upper basin's morphodynamics. [ABSTRACT FROM AUTHOR]

The integration of aerial and ground images is known to be effective for enhancing the quality of 3-D reconstruction in complex urban scenarios. However, directly applying the structure-from-motion (SfM) technique for unified 3-D reconstruction with aerial and ground images is particularly difficult, due to the large differences in viewpoint, scale, and appearance between those two types of images. Previous studies mainly rely on viewpoint rectification or view rendering/synthesis to improve the feature matching quality for aligning the aerial and ground models. Nevertheless, these approaches still fail to address the inherent information differences between aerial and ground images. In this article, we propose a learning-based matching framework for direct SfM with ground and aerial images. The key idea of our method is to learn the pixel-wise consistent features between aerial and ground images to handle the large heterogeneity of these two types of images. Specifically, we deploy a learning-based matching framework to robustly correspond the aerial and ground images. With the high-quality feature matching, learned feature maps are used for refining keypoint locations and fusing featuremetric error into bundle adjustment with the consideration of geometric error, both of which can further improve the accuracy and completeness of the recovered 3-D scene. Extensive experiments conducted on six datasets demonstrate that the proposed method can reconstruct high-fidelity 3-D models with direct aerial-to-ground SfM, which cannot be achieved by existing methods. In addition, our method also shows outstanding performance in subtasks of feature matching and point cloud recovery.

Abstract The new phase of study and revision of the Palaeolithic art of the Cova de les Meravelles, as well as the advances made in the field of 3D documentation in recent years, have motivated a new documentation campaign on the main panel full of tiny engraved motifs. The difficulties involved in acquiring and processing data executed using fine and thin engraving techniques indoors, have been solved through a multi-light photogrammetric survey, photographing the main panel surface with five lighting directions, and applying principal component analysis (PCA). This procedure has allowed us to obtain a large corpus of two and three-dimensional data and to improve the documentation base produced 17 years ago after its discovery. In this paper, we present the procedure followed, the enhanced engraving results in the new documentation campaign, its most significant contributions to the complex archaeological survey, and the limits we have found along the procedure.

Comprehensive documentation is the foundation of effective conservation, repair and maintenance (CRM) practices for architectural heritage. In order to diagnose historic buildings and inform decision making, a combination of multi-disciplinary surveys is fundamental to understanding a building's heritage and performance. Infrared thermography (IRT), a non-contact, non-invasive and non-destructive imaging technique, allows both qualitative and quantitative assessments of temperature to be undertaken. However, the inherent low spatial resolution of thermal imaging has led recent work to fuse thermographic and geometric data for the accurate 3D documentation of architectural heritage. This paper maps the scope of this emerging field to understand the application of IRT and 3D-data fusion (IRT-3DDF) for architectural heritage. A scoping review is undertaken to systematically map the current literature and determine research gaps and future trends. Results indicate that the increasing availability of thermal cameras and advances in photogrammetric software are enabling thermal models to be generated successfully for the diagnosis and holistic management of architectural heritage. In addition, it is evident that IRT-3DDF provides several opportunities for additional data integration, historic building information modelling (H-BIM) and temporal analysis of historic buildings. Future developments are needed to transform IRT-3DDF findings into actionable insights and to apply IRT-3DDF to pressing climate-related challenges, such as energy efficiency, retrofitting and thermal comfort assessments. [ABSTRACT FROM AUTHOR]

Recent technological advances are leading numerous researchers and professional users to the adoption of photogrammetric products for a wide range of geoscientific applications. Especially, drone-based Structure-from-Motion (SfM) photogrammetry is often applied as a high-resolution topographic modelling technique with advantages in terms of time and cost when compared to traditional surveying approaches. The aim of this work, carried out within the GeoArchaeo Sites Project, is to investigate the adaptability of drone-based surveys, even with a targetless approach, and to estimate bathymetrical accuracies in shallow waters. The approach was applied on an underwater site to show the potential for the digitalization and monitoring of an archaeological coastal geosystem in central Italy. Specifically, this work has compared the photogrammetric surveying capability of two drones including a Phantom 4 RTK (‘P4RTK’) and a low-cost Mavic Mini 2 (‘MM2’) and an Olympus TG-6 (underwater camera) for a site submerged with a maximum depth of ∼1.6 m. The assessment of the drone SfM-based products was performed through area-based and point-wise comparisons. Specifically, the area-based were assessed through an underwater photogrammetric survey obtained by acquiring images by an operator snorkeling along a portion of the site of interest. The point-wise comparison was performed using data acquired with a Global Navigation Satellite System (GNSS). This study demonstrates that coupling SfM-photogrammetry and UAS-based surveys have potential to define submerged topography. In particular, the imagery acquired with the P4RTK survey can produce dense 3D models of the underwater surface with high resolution (about 0.02 m) and bathymetric measurements with a vertical accuracy ranging between 0.06 and 0.29 m for the area-based and point-wise analysis, respectively. Thus, the approach adopted and tested involving the use of a P4RTK has the potential to reduce constraints and limitations in terms of GCPs distribution and measurement. Also, with such an approach the need for qualified operators for underwater photogrammetric workflow can be avoided.

Long-term and high-intensity coal mining has led to the increasingly serious surface subsidence and environmental problems. Surface subsidence monitoring plays an important role in protecting the ecological environment of the mining area and the sustainable development of modern coal mines. The development of surveying technology has promoted the acquisition of high-resolution terrain data. The combination of an unmanned aerial vehicle (UAV) point cloud and the structure from motion (SfM) method has shown the potential of collecting multi-temporal high-resolution terrain data in complex or inaccessible environments. The difference of the DEM (DoD) is the main method to obtain the surface subsidence in mining areas. However, the obtained digital elevation model (DEM) needs to interpolate the point cloud into the grid, and this process may introduce errors in complex natural topographic environments. Therefore, a complete three-dimensional change analysis is required to quantify the surface change in complex natural terrain. In this study, we propose a quantitative analysis method of ground subsidence based on three-dimensional point cloud. Firstly, the Monte Carlo simulation statistical analysis was adopted to indirectly evaluate the performance of direct georeferencing photogrammetric products. After that, the operation of co-registration was carried out to register the multi-temporal UAV dense matching point cloud. Finally, the model-to-model cloud comparison (M3C2) algorithm was used to quantify the surface change and reveal the spatio-temporal characteristics of surface subsidence. In order to evaluate the proposed method, four periods of multi-temporal UAV photogrammetric data and a period of airborne LiDAR point cloud data were collected in the Yangquan mining area, China, from 2020 to 2022. The 3D precision map of a sparse point cloud generated by Monte Carlo simulation shows that the average precision in X, Y and Z directions is 44.80 mm, 45.22 and 63.60 mm, respectively. The standard deviation range of the M3C2 distance calculated by multi-temporal data in the stable area is 0.13–0.19, indicating the consistency of multi-temporal photogrammetric data of UAV. Compared with DoD, the dynamic moving basin obtained by the M3C2 algorithm based on the 3D point cloud obtained more real surface deformation distribution. This method has high potential in monitoring terrain change in remote areas, and can provide a reference for monitoring similar objects such as landslides. [ABSTRACT FROM AUTHOR]

This paper proposes an approach of fusion geo-referenced and non-georeferenced data. These data can be acquired by a) unmanned surface vehicles (USVs), b) unmanned aerial vehicles (UAVs), c) airborne and submersible optical sensors, d) acoustic sensors and e) aquatic sound profiling instruments. The test scenario consisted of monitoring and morphological characterisation of a confined water body. As part of the data integration and processing process, object segmentation modules were implemented for tracking and identification of underwater fauna, accompanied by Structure-from-Motion (SfM) and multi-view stereophotogrammetry (MVS) techniques for 3D reconstruction. The results obtained make it possible to recreate morphological models of the study/test areas by coupling all the existing information above and below the water mirror. The spatial resolution obtained is of centimetric order and the resulting data have the ideal quality to be used in underwater mapping and monitoring, as well as in 3D geomorphological measurements of surface water bodies Highlights Multi-source geo-referenced and non-georeferenced data fusion strategy Mapping of surface water bodies using unmanned aerial/aquatic vehicles. Application of segmentation techniques for monitoring and tracking of aquatic fauna. Three-dimensional reconstruction of underwater environments at high spatial resolution Robust framework for the integration of data from passive and active sensors

Structure-from-Motion (SfM) photogrammetry has become a popular solution for three-dimensional topographic data collection in geosciences and can be used for measuring submerged bed surfaces in shallow and clear water systems. However, the performance of through-water SfM photogrammetry has not been fully evaluated for gravel-bed surfaces, which limits its application to the morphodynamics of gravel-bed rivers in both field investigations and flume experiments. In order to evaluate the influence of bed texture, flow rate, ground control point (GCP) layout, and refraction correction (RC) on the measurement quality of through-water SfM photogrammetry, we conducted a series of experiments in a 70 m-long and 7 m-wide flume with a straight artificial channel. Bed surfaces with strongly contrasting textures in two 4 m-long reaches were measured under five constant flow regimes with three GCP layouts, including both dry and underwater GCPs. All the submerged surface models with/without RC were compared with the corresponding dry bed surfaces to quantify their elevation errors. The results illustrated that the poorly sorted gravel-bed led to the better performance of through-water SfM photogrammetry than the bed covered by fine sand. Fine sediment transport caused significant elevation errors, while the static sand dunes and grain clusters did not lead to noticeable errors in the corrected models with dry GCPs. The elevation errors of the submerged models linearly increased with water depth for all the tested conditions of bed textures, GCP layouts, and discharges in the uncorrected models, but the slopes of the increasing relations varied with texture. The use of underwater GCPs made significant improvements to the performance of direct through-water SfM photogrammetry, but counteracted with RC. The corrected models with dry GCPs outperformed the uncorrected ones with underwater GCPs, which could still be used to correct the underestimation in surface elevation caused by RC. Based on the new findings, recommendations for through-water SfM photogrammetry in measuring submerged gravel-bed surfaces were provided. [ABSTRACT FROM AUTHOR]

The new phase of study and revision of the Palaeolithic art of the Cova de les Meravelles, as well as the advances made in the field of 3D documentation in recent years, have motivated a new documentation campaign on the main panel full of tiny engraved motifs. The difficulties involved in acquiring and processing data executed using fine and thin engraving techniques indoors, have been solved through a multi-light photogrammetric survey, photographing the main panel surface with five lighting directions, and applying principal component analysis (PCA). This procedure has allowed us to obtain a large corpus of two and three-dimensional data and to improve the documentation base produced 17 years ago after its discovery. In this paper, we present the procedure followed, the enhanced engraving results in the new documentation campaign, its most significant contributions to the complex archaeological survey, and the limits we have found along the procedure. [ABSTRACT FROM AUTHOR]

Arctic tundra environments are characterized by a spatially heterogeneous end-of-winter snow depth resulting from wind transport and deposition. Traditional methods for measuring snow depth do not accurately capture such heterogeneity at catchment scales. In this study we address the use of high-resolution, spatially distributed, snow depth data for Arctic environments through the application of unmanned aerial systems (UASs). We apply Structure-from-Motion photogrammetry to images collected using a fixed-wing UAS to produce a 1 m resolution snow depth product across seven areas of interest (AOIs) within the Trail Valley Creek Research Watershed, Northwest Territories, Canada. We evaluated these snow depth products with in situ measurements of both the snow surface elevation (n = 8434) and snow depth (n = 7191). When all AOIs were averaged, the RMSE of the snow surface elevation models was 0.16 m (

We studied some exposures of the Roccacaramanico Conglomerate (RCC), a calcareous-clastic mega-bed intercalated within the Late Messinian–Early Pliocene pelitic succession of the La Queglia and Maiella tectonic units (central Apennines). The outcrops, localized in the overturned limb of a kilometric-scale syncline, show a complex array of fractures, including multiple systems of closely spaced cleavages, joints, and mesoscopic faults, which record the progressive deformation associated with the Late Pliocene thrusting. Due to the extent of the investigated sites and a large amount of data to collect, we applied a multi-methodology survey technique integrating unmanned aerial vehicle (UAV) technologies and digital mapping in the field. We reconstructed the 3D digital outcrop model of the RCC in the type area and defined the 3D pattern of fractures and their time–space relationships. The field survey played a pivotal role in determining the various sets of structures, their kinematics, the associated displacements, and relative chronology. The results unveiled the investigated area's tectonic evolution and provide a deformation model that could be generalized in similar tectonic contexts. Furthermore, the methodology allows for evaluating the reliability of the applied remote survey techniques (i.e., using UAV) compared to those based on the direct measurements of structures using classic devices. Our purpose was to demonstrate that our multi-methodology approach can describe the tectonic evolution of the study area, providing consistent 3D data and using a few ground control points. Finally, we propose two alternative working methods and discuss their different fields of application. [ABSTRACT FROM AUTHOR]

A pesar de los grandes daños causados por los eventos de lluvia de El Niño-Oscilación del Sur (ENSO), sigue habiendo una escasez de registros de caudales de inundaciones repentinas en Manta, una de las ciudades más grandes de Ecuador. Para abordar tal brecha de datos, esta investigación presenta una nueva línea de flujo de trabajo para hacer uso de datos de fuentes múltiples; en específico, combinamos modelos hidráulicos bidimensionales (2D) convencionales con Structure-from-Motion (SfM) y videos de YouTube para obtener estimaciones indirectas fiables de caudal máximo para un río efímero no aforado. Las estimaciones se realizan posterior a las inundaciones. Se obtuvo un modelo de elevación digital (DEM) de resolución submétrica del tramo del río utilizando SfM; los niveles de agua máximos "observados" de los videos de YouTube se compararon con varias simulaciones hidráulicas en 2D para encontrar el mejor ajuste dentro de 0.43 m. Las observaciones de la velocidad del agua confirmaron esas estimaciones de caudal. La investigación en este documento indica que los flujos de trabajo novedosos que incluyen datos de código abierto disponibles gratuitamente en las plataformas de redes sociales pueden proporcionar estimaciones indirectas de caudales máximos y, en lo posterior, pueden convertirse en una herramienta para gestión de riesgo de inundaciones

Comprehensive documentation is the foundation of effective conservation, repair and maintenance (CRM) practices for architectural heritage. In order to diagnose historic buildings and inform decision making, a combination of multi-disciplinary surveys is fundamental to understanding a building’s heritage and performance. Infrared thermography (IRT), a non-contact, non-invasive and non-destructive imaging technique, allows both qualitative and quantitative assessments of temperature to be undertaken. However, the inherent low spatial resolution of thermal imaging has led recent work to fuse thermographic and geometric data for the accurate 3D documentation of architectural heritage. This paper maps the scope of this emerging field to understand the application of IRT and 3D-data fusion (IRT-3DDF) for architectural heritage. A scoping review is undertaken to systematically map the current literature and determine research gaps and future trends. Results indicate that the increasing availability of thermal cameras and advances in photogrammetric software are enabling thermal models to be generated successfully for the diagnosis and holistic management of architectural heritage. In addition, it is evident that IRT-3DDF provides several opportunities for additional data integration, historic building information modelling (H-BIM) and temporal analysis of historic buildings. Future developments are needed to transform IRT-3DDF findings into actionable insights and to apply IRT-3DDF to pressing climate-related challenges, such as energy efficiency, retrofitting and thermal comfort assessments.

• Direct georeferencing with onboard sensors is an advantage in UAV-based mapping. • Indirect georeferencing can be replaced by a direct method with n-RTK or PPK positioning. • Accuracies of n-RTK, PPK and PPP in direct georeferencing are sufficient for large-scale mapping. • PPP method in UAV-based mapping has practical advantages compared to relative positioning. • RT-PPP in UAV-based mapping has the potential for accuracy improvement. Uncrewed Aerial Vehicle (UAV) photogrammetry is a very efficient, low-cost approach for mapping small or medium-sized local areas. This approach is adequate for achieving the needed accuracy in many applications, and onboard positioning sensors have made this technique more feasible by decreasing the need for ground control stations (GCPs). When real-time positioning strategies are used, photogrammetric mapping can also be performed instantaneously in the field. This is especially crucial in applications like disaster management, where current situation must be detected rapidly. It is critical to choose a georeferencing approach in applications that is appropriate for the physical parameters of the mapping area, the intended use of the photogrammetric mapping, and the needed accuracy. In order to accurately georeference the photogrammetric outputs, this study assesses the real-time and post-processed performances of absolute and relative GNSS positioning approaches. Finally, it compares these strategies in terms of the position accuracy of the created models. In the evaluations, the 3D models of the study area were created utilizing network-based Real Time Kinematic (n-RTK), Post-Processed Kinematic (PPK) Positioning, and Precise Point Positioning (PPP) approaches, with direct georeferencing of the UAV-based photogrammetric images. These models were then compared to models developed using traditional indirect georeferencing with GCPs and aided indirect georeferencing. According to the findings, direct georeferencing using the n-RTK and PPK techniques provided a horizontal position accuracy in terms of root mean square errors ( r m s 2 D = 1.6 cm and 1.9 cm) almost equal to indirect georeferencing using GCPs ( r m s 2 D = 1.9 cm). In terms of vertical position accuracies, the n-RTK and PPK approaches in direct georeferencing ( r m s h = 3.7 cm and 3.9 cm, respectively) produced rather different results than the GCPs-based conventional strategy ( r m s h = 1.6 cm), although the discrepancies were within acceptable error limits. Direct georeferencing with the PPP approach produced the lowest horizontal and vertical position accuracies ( r m s 2 D = 5.9 cm, r m s h = 5.7 cm). However, this level of accuracy was deemed adequate for use in large-scale mapping in Turkey's authorized regulation. [ABSTRACT FROM AUTHOR]

This paper shows results of comparing performances of four unmanned aircraft systems (UAS) in terms of photogrammetric survey's quality. This study aims to investigate what is the more suitable UAS for specific applications considering the required scale factor, such as for architectural, environmental, and restoration purposes. A series of photogrammetric surveys were conducted in a hilly area of about 5 ha using Phantom 4 Adv, Mavic 2 Pro, Mavic Air 2, and Mavic Mini 2. These unmanned aircrafts are commercial user–grade systems used mainly by private professionals. Several photogrammetric reconstructions were performed by varying essential parameters, such as flight altitude and cameras of remotely piloted aircraft systems (RPAS), applying structure-from-motion (SfM) algorithms to the images taken from the UAS. The surveys' quality was analyzed by comparing the ground targets' coordinates extrapolated from the point clouds to those measured on the field with indirect georeferencing through GNSS technology. Fifty targets were installed and arranged following a reasonably regular mesh. The boundary conditions were maintained the same for each flight mission, flight trajectories, and the ground control point distribution on the ground. For each survey made by each of the four UAS, altimetric and planimetric residuals were reported and compared. Average residuals from Phantom 4 Adv, about 15 mm, almost disappear compared to the other UASs; the discrepancy is one order of magnitude. With a regular grid geometry of ground targets, the Mavic Mini 2 led to an error average of about 5 cm. Remembering that the Mavic Mini 2 is an ultralight drone (does not require a pilot's license), it could significantly reduce cost compared to the other systems. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) images have become the main remote sensing data sources for varying applications, and structure from motion (SfM) is the golden standard for resuming camera poses. Matching local feature descriptors is the prerequisite for the accurate and complete orientation of UAV images. Recently, some newly proposed learned methods have been shown to outperform the hand-crafted methods, such as the scale invariant feature transform (SIFT) and its variants, and almost all learned methods have been trained and evaluated by using images from the internet with varying focal lengths and varying size. It is of interest to investigate the performance of these learned methods with their pretrained models for feature detection and description in the context of the SfM-based orientation. Thus, this article conducts a comprehensive evaluation of both advanced hand-crafted and newly proposed learned detectors and descriptors by using four UAV datasets. The performance of these selected methods is compared in the context of feature matching and the SfM and (multiview stereo) MVS-based reconstruction. Experimental results demonstrate that the learned descriptors combined with the SIFT-like detectors can provide accurate and complete feature correspondences and achieve better or competitive performance in the SfM and MVS-based reconstruction. For UAV image orientation, the learned descriptors can be an alternative to the existing hand-crafted descriptors without their model retraining. The source codes of this evaluation would be made publicly available.

Terrestrial laser scanning (TLS) and drone-based structure-from-motion (SfM) photogrammetry allowed the study of soil deformations due to blast-induced liquefaction during an experiment carried out on 4 June 2018. The research aimed at both evaluating the measurement quality and estimating the rammed aggregate piers (RAPs) effectiveness in mitigating the effects of soil liquefaction. These effects mainly consist of subsidence and deposits of ejected and extruded materials. The comparison between multitemporal 3D models provided surface variation maps and volume changes. In addition, classical topographical leveling allowed the measurement of subsurface vertical displacement along a specific cross section. The results pointed out a significant reduction, higher than 50% of soil deformation in areas improved by RAPs installation; moreover, the corresponding volume variations were no more than about 37% of those occurred in the not improved area. Finally, a critical comparison between remote sensing and leveling suggested that surface variation maps could underestimate the area lowering up to 15% in this kind of terrain. [ABSTRACT FROM AUTHOR]

Ice doline is a particular kind of ice morphology, usually scattered on ice streams which are mostly far from the existing research bases. For this reason, glaciologists rarely have opportunities to document its developments in detail. Satellite observations are too coarse to capture such fine features, whereas unmanned aerial vehicle (UAV)-based structure-from-motion (SfM) and light detection and ranging (LiDAR) technologies have revolutionized geosciences research, especially in less accessed polar regions. We developed two bespoke UAV systems for glaciological investigation and carried out four campaigns during two consecutive Chinese Antarctic expeditions in 2017 and 2018. Founded on manual coregistration and accuracy assessment, a successful application to characterize a doline's spatio-temporal evolution is presented in this article. The overlying weight of surface melting directly triggered the collapse event on Jan 30, 2017 near the Dalk Glacier, and then the newborn doline grew for another 8135.6 m2 in area and 280 303.38 m3 in volume by early 2018. The UAV-based results revealed the doline's changes during a year, showing a maximum horizontal extension of 50 m and vertical subsidence of more than 10 m. Furthermore, we evaluate the photogrammetry and LiDAR systems and find the former is cost-effective and time-efficient on a large-scale survey, while the latter enjoys a better capability to characterize ice morphological details. Based on systematic comparisons, other pros and cons of the two techniques are discussed. To achieve the best performance for relevant applications in similar scenarios, we recommend adopting an integrated approach, in which the LiDAR restores the fine features on the basis of extensive SfM coverage.

Long-term and high-intensity coal mining has led to the increasingly serious surface subsidence and environmental problems. Surface subsidence monitoring plays an important role in protecting the ecological environment of the mining area and the sustainable development of modern coal mines. The development of surveying technology has promoted the acquisition of high-resolution terrain data. The combination of an unmanned aerial vehicle (UAV) point cloud and the structure from motion (SfM) method has shown the potential of collecting multi-temporal high-resolution terrain data in complex or inaccessible environments. The difference of the DEM (DoD) is the main method to obtain the surface subsidence in mining areas. However, the obtained digital elevation model (DEM) needs to interpolate the point cloud into the grid, and this process may introduce errors in complex natural topographic environments. Therefore, a complete three-dimensional change analysis is required to quantify the surface change in complex natural terrain. In this study, we propose a quantitative analysis method of ground subsidence based on three-dimensional point cloud. Firstly, the Monte Carlo simulation statistical analysis was adopted to indirectly evaluate the performance of direct georeferencing photogrammetric products. After that, the operation of co-registration was carried out to register the multi-temporal UAV dense matching point cloud. Finally, the model-to-model cloud comparison (M3C2) algorithm was used to quantify the surface change and reveal the spatio-temporal characteristics of surface subsidence. In order to evaluate the proposed method, four periods of multi-temporal UAV photogrammetric data and a period of airborne LiDAR point cloud data were collected in the Yangquan mining area, China, from 2020 to 2022. The 3D precision map of a sparse point cloud generated by Monte Carlo simulation shows that the average precision in X, Y and Z directions is 44.80 mm, 45.22 and 63.60 mm, respectively. The standard deviation range of the M3C2 distance calculated by multi-temporal data in the stable area is 0.13–0.19, indicating the consistency of multi-temporal photogrammetric data of UAV. Compared with DoD, the dynamic moving basin obtained by the M3C2 algorithm based on the 3D point cloud obtained more real surface deformation distribution. This method has high potential in monitoring terrain change in remote areas, and can provide a reference for monitoring similar objects such as landslides.

Olive groves are one of the main agroecosystems in the Mediterranean region, but water erosion, aggravated by inappropriate soil management, is compromising the environmental sustainability of these crops. National and international public organisations, including the European Union via its Common Agricultural Policy, have acknowledged the problem and recognise the need to quantify the effects of this process. However, the variability of currently available short-term soil erosion measurements, together with limited understanding of the underlying processes, mean there is considerable uncertainty about the long-term effects of soil erosion. This paper presents an innovative procedure called SERHOLIVE4.0 designed to measure and model long-term soil erosion rates in olive groves, by means of structure-from-motion (SfM) techniques by which image information is obtained from unmanned aerial vehicles (UAVs). For the present study, the procedure was evaluated in mountain olive groves, where the erosion rate was calculated from historical surface reconstructions. Overall, this approach was found to be practical and effective. The method includes the following steps: [1] measure the current relief using UAV technology; [2] reconstruct the historical relief from field measurements; [3] calculate soil truncation (h) and obtain a soil erosion rate map; [4] determine the erosive dynamics of the slope and establish the relation between tree truncation, slope and mounds. The method we describe presents the following advantages:•it quantifies soil losses by reference to existing tree mounds;•it is straightforward to apply;•its application enhances the calibration of erosion models., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier B.V.)

Structure-from-Motion (SfM) photogrammetry has become a popular solution for three-dimensional topographic data collection in geosciences and can be used for measuring submerged bed surfaces in shallow and clear water systems. However, the performance of through-water SfM photogrammetry has not been fully evaluated for gravel-bed surfaces, which limits its application to the morphodynamics of gravel-bed rivers in both field investigations and flume experiments. In order to evaluate the influence of bed texture, flow rate, ground control point (GCP) layout, and refraction correction (RC) on the measurement quality of through-water SfM photogrammetry, we conducted a series of experiments in a 70 m-long and 7 m-wide flume with a straight artificial channel. Bed surfaces with strongly contrasting textures in two 4 m-long reaches were measured under five constant flow regimes with three GCP layouts, including both dry and underwater GCPs. All the submerged surface models with/without RC were compared with the corresponding dry bed surfaces to quantify their elevation errors. The results illustrated that the poorly sorted gravel-bed led to the better performance of through-water SfM photogrammetry than the bed covered by fine sand. Fine sediment transport caused significant elevation errors, while the static sand dunes and grain clusters did not lead to noticeable errors in the corrected models with dry GCPs. The elevation errors of the submerged models linearly increased with water depth for all the tested conditions of bed textures, GCP layouts, and discharges in the uncorrected models, but the slopes of the increasing relations varied with texture. The use of underwater GCPs made significant improvements to the performance of direct through-water SfM photogrammetry, but counteracted with RC. The corrected models with dry GCPs outperformed the uncorrected ones with underwater GCPs, which could still be used to correct the underestimation in surface elevation caused by RC. Based on the new findings, recommendations for through-water SfM photogrammetry in measuring submerged gravel-bed surfaces were provided.

We studied some exposures of the Roccacaramanico Conglomerate (RCC), a calcareous-clastic mega-bed intercalated within the Late Messinian–Early Pliocene pelitic succession of the La Queglia and Maiella tectonic units (central Apennines). The outcrops, localized in the overturned limb of a kilometric-scale syncline, show a complex array of fractures, including multiple systems of closely spaced cleavages, joints, and mesoscopic faults, which record the progressive deformation associated with the Late Pliocene thrusting. Due to the extent of the investigated sites and a large amount of data to collect, we applied a multi-methodology survey technique integrating unmanned aerial vehicle (UAV) technologies and digital mapping in the field. We reconstructed the 3D digital outcrop model of the RCC in the type area and defined the 3D pattern of fractures and their time–space relationships. The field survey played a pivotal role in determining the various sets of structures, their kinematics, the associated displacements, and relative chronology. The results unveiled the investigated area’s tectonic evolution and provide a deformation model that could be generalized in similar tectonic contexts. Furthermore, the methodology allows for evaluating the reliability of the applied remote survey techniques (i.e., using UAV) compared to those based on the direct measurements of structures using classic devices. Our purpose was to demonstrate that our multi-methodology approach can describe the tectonic evolution of the study area, providing consistent 3D data and using a few ground control points. Finally, we propose two alternative working methods and discuss their different fields of application.

Sediments accumulated in check dams are a valuable measure to estimate soil erosion rates. Here, geographic information systems (GIS) and three machine learning techniques (MARS-multivariate adaptive regression splines, RF-random forest and SVM-support vector machine) were used, for the first time, to predict sediment deposition rate (SR) in check-dams located in six watersheds in SW Spain. There, 160 dry-stone check dams (~ 77.8 check-dams km−2), accumulated sediments during a period that varied from 11 to 23 years. The SR was estimated in former research using a topographical method and a high-resolution Digital Elevation Model (DEM) (average of 0.14 m3 ha−1 year−1). Nine environmental-topographic parameters were calculated and employed as predictors of the SR. The ability of MARS, RF and SVM was evaluated by using a five-fold cross-validation, considering the entire area (ALL), the check dams on the hillslope (HILL) and the valley-bottoms (VALLEY), as well as the three catchments (B, C and D) with the highest number of check dams. The accuracy of the models was assessed by the relative root mean square error (RRMSE) and the mean absolute error (MAE). The results revealed that RF and SVM are able to predict SR with higher and more stable accuracy than MARS. This is evident for the datasets ALL, VALLEY and D, where errors of prediction exhibited by MARS were from 44 to 77% (RRMSE) and from 37 to 62% (MAE) higher than those achieved by RF and SVM, but it also held for the datasets HILL and B where the difference of RRMSE and MAE was 7–10% and 12–17%, respectively. [ABSTRACT FROM AUTHOR]

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Recently, multirotor UAVs have been widely used in high-precision terrain mapping, cadastral surveys and other fields due to their low cost, flexibility, and high efficiency. Indirect georeferencing of ground control points (GCPs) is often required to obtain highly accurate topographic products such as orthoimages and digital surface models. However, in practical projects, GCPs are susceptible to anomalies caused by external factors (GCPs covered by foreign objects such as crops and cars, vandalism, etc.), resulting in a reduced availability of UAV images. The errors associated with the loss of GCPs are apparent. The widely used solution of using natural feature points as ground control points often fails to meet the high accuracy requirements. For the problem of control point anomalies, this paper innovatively presents two new methods of completing data fusion by supplementing photos via UAV at a later stage. In this study, 72 sets of experiments were set up, including three control experiments for analysis. Two parameters were used for accuracy assessment: Root Mean Square Error (RMSE) and Multiscale Model to Model Cloud Comparison (M3C2). The study shows that the two new methods can meet the reference accuracy requirements in horizontal direction and elevation direction (RMSEX = 70.40 mm, RMSEY = 53.90 mm, RMSEZ = 87.70 mm). In contrast, the natural feature points as ground control points showed poor accuracy, with RMSEX = 94.80 mm, RMSEY = 68.80 mm, and RMSEZ = 104.40 mm for the checkpoints. This research considers and solves the problems of anomalous GCPs in the photogrammetry project from a unique perspective of supplementary photography, and proposes two new methods that greatly expand the means of solving the problem. In UAV high-precision projects, they can be used as an effective means to ensure accuracy when the GCP is anomalous, which has significant potential for application promotion. Compared with previous methods, they can be applied in more scenarios and have higher compatibility and operability. These two methods can be widely applied in cadastral surveys, geomorphological surveys, heritage conservation, and other fields.

Flood damages occur when just one inch of water enters a residential household and models of flood damage estimation are sensitive to first-floor elevation (FFE). The current sources for FFEs consist of costly survey-based elevation certificates (ECs) or assumptions based on year built, foundation type, and flood zone. We sought to address these limitations by establishing the role of an Unmanned Aerial System (UAS) to efficiently derive accurate FFEs. Four residential communities within Galveston Island, Texas were selected to assess efficient flight parameters required for UAS photogrammetry within the built environment. A real-time kinematic positioning enabled (RTK) UAS was then used to gather georeferenced aerial imagery and create detailed 3D photogrammetric models with ±0.02 m horizontal and ±0.05 m vertical accuracies. From these residential models, FFEs and other structural measurements present in traditional ECs were obtained. Comparative statistical analyses were performed using the UAS-based measurements and traditional EC measurements. UAS based FFE measurements achieved 0.16 m mean absolute error (MAE) across all comparative observations and were not statistically different from traditional EC measures. We conclude the RTK enabled UAS approach is an efficient, cost-effective method in establishing accurate FFEs and other flood-sensitive measures in residential communities.

Comparing photogrammetric performances of four user-grade unmanned aircraft systems (UAS) is the main aim of this paper. This study investigates what is the more suitable UAS for specific applications considering the required scale factor, such as for architectural, environmental and restoration purposes. Some photogrammetric surveys were conducted in a 5 ha area using a Phantom 4 Adv, Mavic 2 Pro, Mavic Air 2 and Mavic Mini 2. These unmanned aircrafts are commercial systems used mainly by private professionals. Some photogrammetric reconstructions were carried out by varying flight altitude and camera settings of the 4 UAS. Structure-from-motion (SfM) algorithms were applied to the images taken from the UASs. The surveys’ quality was analyzed by comparing the ground targets’ coordinates measured on the field with indirect georeferencing through global navigation satellite system (GNSS). Fifty targets were installed and arranged following a kind of regular grid. For each photogrammetric flight, the boundary conditions were maintained the same, as well as the flight trajectories and the ground control point distribution. Altimetric and planimetric residuals were reported and compared for each photogrammetric survey. Using a regular grid of ground targets, the result obtained from Phantom 4 is one order of magnitude better than the ones obtained from the other UASs. Mavic Mini 2 leads to an error average of about 5 cm. Remembering that the Mavic Mini 2 is an ultralight drone (it does not require a pilot’s license), it could significantly reduce costs compared to all the others.

The quality of 3D scene reconstruction and monitoring through structure‐from‐motion multiview stereo (SfM‐MVS) depends on critical key factors, including camera calibration and image network geometry. The goal of this paper is to examine the monitoring ability of an SfM‐MVS workflow based on four or more ground‐based digital single‐lens reflex (DSLR) cameras and to estimate differences when adopting both fixed and variable camera positions and orientations. This was achieved by conducting work on a scaled laboratory testfield and a sea cliff. Tests demonstrate that a monitoring system using just four fixed cameras can achieve valuable monitoring capabilities and tolerate imperfections in the camera calibration. Furthermore, such a configuration can achieve accuracies comparable to terrestrial laser scanning (TLS) and drone‐based photogrammetry. The study demonstrates that minimising registration errors between point clouds is critical. The "registration SIFT" approach could resolve such problems. [ABSTRACT FROM AUTHOR]

[Display omitted] • A simple method that contemplates all the factors involved in the erosive process. • UAV-SfM technology successfully quantifies the historical rate of soil loss. • The slope profile and the distance to the mound condition the truncation. • The mound reduces truncation in uphill areas. Soil water erosion is one of the most important environmental problems for the sustainability of Mediterranean olive groves on hillsides. Governments and public agencies recognize the need to control this process in order to improve soil conservation, especially in vulnerable areas. In the present study, a simple, inexpensive method using Structure-from-motion (SfM) and Unmanned Aerial Vehicle (UAV) technology was applied to quantify the soil loss rates provoked by water erosion and tillage in mountain olive groves, according to a reconstruction of their historical surface features. Specifically, the main study aims were: i) to quantify the historical soil loss in olive groves, by analysing residual tree mounds; ii) to consider how soil relief and management can influence the erosion process; iii) to determine the degree to which the proposed method achieves the above aims. Analysis revealed a mean erosion rate in the study area of 127.69 t ha−1 years−1, with a linear relationship between soil truncation and slope (R2 = 0.64, p < 0.001). The highest soil loss rates (-1.67 ± 0.48 m) occurred in areas where the slopes were steepest (22.36 ± 4.46 %) and the longitudinal profile rectilinear. Erosion rates are determined not only by the slope, but also by its position and distance from the tree mound. This study highlights the need for differential management based on slope-feature considerations, in order to limit soil losses. Overall, the tool presented to support decision making provides an effective method for calculating erosion rates. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are increasingly used for many scientific applications, including archaeological surveys. We test the suitability and practicability of UAV surveying in the tropical lowlands of Brazil and techniques for visualizing the resulting digital elevation models, specifically the Red Relief Image Map (RRIM). We present the results of UAV surveys conducted at four diverse archaeological earthwork sites situated in interfluvial southwestern Amazonia, in the state of Acre. The elevation models produced from UAV derived point clouds display clear patterns in the site layouts and reveal subtle intra‐site earthwork features that are not easily discernible on the ground. Our study demonstrates that UAVs are cost efficient and give highly detailed results for topographic mapping and visualization of archaeological features when vegetation cover is sufficiently low and sparse. The rapid data capture and lack of spatial sampling bias of the UAV data collection is a significant advantage compared to conventional mapping methods. Furthermore, UAV surveying and UAV derived data processing do not require expensive technologies or specialized user expertise, since open‐source software and easy‐to‐use toolkits are readily available. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we present a new fast and robust method for structure from motion (SfM) for data sets potentially comprising thousands of ordered or unordered images. Our work focuses on the two most time-consuming procedures: (a) image matching and (b) pose estimation. For image matching, a new method employing a random k-d forest is proposed to quickly obtain pairs of overlapping images from an unordered set. After that, image matching and the estimation of relative orientation parameters are performed only for pairs found to be very likely to overlap. For pose estimation, we use a two-stage global approach, separating the determination of rotation matrices and translation parameters; the latter are computed simultaneously using a new method. In order to cope with outliers in the relative orientations, which global approaches are particularly sensitive to, we present a new constraint based on triplet loop closure errors of rotation and translation. Finally, a robust bundle adjustment is carried out to refine the image orientation parameters. We demonstrate the potential and limitations of our pipeline using various real-world datasets including ordered image data acquired from UAV (unmanned aerial vehicle) and other platforms as well as unordered data from the internet. The experiments show that our work performs better than comparable state-of-the-art SfM systems in terms of run time, while we achieve a similar accuracy and robustness. [ABSTRACT FROM AUTHOR]

Uncrewed aerial systems (UAS) provide an effective method to examine geomorphic and vegetation change in restored coastal dune ecosystems. Coupling structure-from-motion (SfM) photogrammetry with RGB orthomosaic imagery allows researchers to characterize spatial-temporal geomorphic responses associated with differences in vegetation cover. Such approaches provide quantitative data on landscape morphodynamics and sediment erosion and deposition responses that allow scientists and land managers to assess the efficacy of dynamic restoration efforts and, in turn, make informed decisions for future restoration projects. Two different restored coastal foredune sites in Humboldt County, California were monitored between 2016–20 with UAS (quadcopter and fixed-wing), kite aerial photogrammetry (KAP), and terrestrial laser scanning (TLS) platforms. We compared our KAP- and UAS-SfM elevation models to concurrently collected TLS bare earth models for five of our fifteen collections. The goal of this study was to inform on the potential of a multi-platform aerial approach for calculating geomorphic differences (i.e., topographic differencing), in order to quantify sediment erosion and deposition, and vegetation change over a coastal dune ecosystem. While UAS-SfM datasets were relatively well fit to their TLS counterparts (2.1–12.2% area of difference), the KAP-SfM surfaces exhibited higher deviations (23.6–27.6%) and suffered from systematic collection inconsistencies related to methods and susceptibility to external factors (e.g., the influence of wind speed and direction on variable altitude, image overlap, and coverage extent). Finally, we provide commentary on the logistical considerations regarding KAP and UAS data collection and the construction of uncertainty budgets for geomorphic change detection (GCD), while providing suggestions for standardizing methods for uncertainty budgeting. While we propose an approach that incorporates multiple levels of collection- and processing-based uncertainty, we also recognize that uncertainty is often project-specific and outline the development of potential standards for incorporating uncertainty budgets in SfM projects.

The 3D digitization and Building Information Modeling (BIM), which is based on parametric objects, have considerably advanced by developing massive data capture techniques. Thus, reverse engineering currently plays a major role as these technologies capture accurately and efficiently the geometry, color and textures of complex architectural, archaeological and cultural heritage. This paper aims to validate close-range Structure from Motion (SfM) for heritage by analyzing the point density and the 3D mesh geometry in comparison with Terrestrial Laser Scanning (TLS). The accuracy of the results and the geometry mainly depends on the processing performed on the point set. Therefore, these two variables are significant in the 3D reconstruction of heritage buildings. This paper focuses on a 15th century case study in Seville (Spain): the main façade of Casa de Pilatos. Ten SfM surveys were carried out varying the capture method (simple and stereoscopic) and the number of shots, distances, orientation and procedure. A mathematical analysis is proposed to verify the point spatial resolution and the accuracy of the 3D model geometry by section profiles in SfM data. SfM achieved acceptable accuracy levels to generate 3D meshes despite disordered shots and the number of images. Hence, stereoscopic photography using new instruments improved the results of close-range photogrammetry while reducing the required number of photographs.

Unmanned aerial vehicle (UAV) photogrammetry has recently emerged as a popular solution to obtain certain products necessary in linear projects, such as orthoimages or digital surface models. This is mainly due to its ability to provide these topographic products in a fast and economical way. In order to guarantee a certain degree of accuracy, it is important to know how many ground control points (GCPs) are necessary and how to distribute them across the study site. The purpose of this work consists of determining the number of GCPs and how to distribute them in a way that yields higher accuracy for a corridor-shaped study area. To do so, several photogrammetric projects have been carried out in which the number of GCPs used in the bundle adjustment and their distribution vary. The different projects were assessed taking into account two different parameters: the root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2). From the different configurations tested, the projects using 9 and 11 GCPs (4.3 and 5.2 GCPs km−1, respectively) distributed alternatively on both sides of the road in an offset or zigzagging pattern, with a pair of GCPs at each end of the road, yielded optimal results regarding fieldwork cost, compared to projects using similar or more GCPs placed according to other distributions.

Due to the recent improvements of automatic measurement procedures in photogrammetry, multi-view 3D reconstruction technologies are becoming a favourite survey tool. Rapidly widening structure-from-motion (SfM) software packages offer significantly easier image processing workflows than traditional photogrammetry packages. However, while most orientation and surface reconstruction strategies will almost always succeed in any given task, estimating the quality of the result is, to some extent, still an open issue. An assessment of the precision and reliability of block orientation is necessary and should be included in every processing pipeline. Such a need was clearly felt from the results of close-range photogrammetric surveys of in situ full-scale and laboratory-scale experiments. In order to study the impact of the block control and the camera network design on the block orientation accuracy, a series of Monte Carlo simulations was performed. Two image block configurations were investigated: a single pseudo-normal strip and a circular highly-convergent block. The influence of surveying and data processing choices, such as the number and accuracy of the ground control points, autofocus and camera calibration was investigated. The research highlights the most significant aspects and processes to be taken into account for adequate in situ and laboratory surveys, when modern SfM software packages are used, and evaluates their effect on the quality of the results of the surface reconstruction.

Abstract: The recent development of structure‐from‐motion (SfM) and multi‐view stereo (MVS) photogrammetry techniques has enabled semi‐automatic high‐resolution bathymetry using aerial images taken by consumer‐grade digital cameras mounted on unmanned aerial vehicles (UAVs). However, the applicability of these techniques is sometimes limited by sun and sky reflections at the water surface, which render the point‐cloud density and accuracy insufficient. In this research, we present a new imaging technique to suppress the effect of these water‐surface reflections. In this technique, we order a drone to take a short video instead of a still picture at each waypoint. We then apply a temporal minimum filter to the video. This filter extracts the smallest RGB values in all the video frames for each pixel, and composes an image with greatly reduced reflection effects. To assess the performance of this technique, we applied it at three small shallow‐water sites. Specifically, we evaluated the effect of the technique on the point cloud density and the accuracy and precision of the photogrammetry. The results showed that the proposed technique achieved a far denser point cloud than the case in which a randomly chosen frame was used for each waypoint, and also showed better overall accuracy and precision in estimating water‐bottom elevation. The effectiveness of this new technique should depend on the surface wave state and sky radiance distribution, and this dependence, as well as the applicability to large areas, should be investigated in future research. Copyright © 2018 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Structure-from-motion with multi-view stereo (SfM-MVS) methods hold the potential for monitoring and quantifying cliff erosion to levels of accuracy and precision which rival terrestrial laser scanning (TLS) and at a fraction of the cost. We benchmark repeat SfM-MVS against TLS for quantifying rock fall frequency, volume, and cliff face erosion rates for a ∼1 km section of coastal cliffs where cliff top infrastructure is threatened by erosion. First, we address a major unknown in these techniques, the number and configuration of control points. Surveys demonstrate that a sparse configuration along the cliff base and top, at spacing equivalent to the cliff height, provides suitable accuracy at acceptable logistic time and expense. Second, we show that SfM-MVS models match equivalent TLS data to within 0.04 m, and that the correlation between intersecting TLS- and SfM-derived rock fall volumes improves markedly above a detection threshold of 0.07 m 3 . Rock falls below this size threshold account for ∼77.7% of detected rock falls but only 1.9% of the calculated annual eroded volume. Annual erosion rates for the 1 km cliff face as calculated by repeat TLS and SfM differencing are 0.6 × 10 −2  m a −1 and 0.7 × 10 −2  m a −1 , respectively. Kilometre-scale patterns of cliff erosion are dominated by localised zones of high-magnitude, episodic failure that are over an order of magnitude greater than background rates. The ability of non-specialist engineers, geologists, geomorphologists and managers to rapidly capture high quality, accurate erosion data in a cost-effective manner through repeat SfM-MVS has significant potential to inform coastal managers and decision makers. To further empower coastal authorities and communities, policy frameworks must be developed to incorporate and interpret these data. [ABSTRACT FROM AUTHOR]

Glacial landsystems in the high-Arctic have been reported to undergo geomorphological transformation during deglaciation. This research evaluates moraine evolution over a decadal timescale at Midtre Lovénbreen, Svalbard. This work is of interest because glacial landforms developed in Svalbard have been used as an analogue for landforms developed during Pleistocene mid-latitude glaciation. Ground penetrating radar was used to investigate the subsurface characteristics of moraines. To determine surface change, a LiDAR topographic data set (obtained 2003) and a UAV-derived (obtained 2014) digital surface model processed using structure-from-motion (SfM) are also compared. Evaluation of these data sets together enables subsurface character and landform response to climatic amelioration to be linked. Ground penetrating radar evidence shows that the moraine substrate at Midtre Lovénbreen includes ice-rich (radar velocities of 0.17 m ns −1 ) and debris-rich (radar velocities of 0.1–0.13 m ns −1 ) zones. The ice-rich zones are demonstrated to exhibit relatively high rates of surface change (mean thresholded rate of −4.39 m over the 11-year observation period). However, the debris-rich zones show a relatively low rate of surface change (mean thresholded rate of −0.98 m over the 11-year observation period), and the morphology of the debris-rich landforms appear stable over the observation period. A complex response of proglacial landforms to climatic warming is shown to occur within and between glacier forelands as indicated by spatially variable surface lowering rates. Landform response is controlled by the ice-debris balance of the moraine substrate, along with the topographic context (such as the influence of meltwater). Site-specific characteristics such as surface debris thickness and glaciofluvial drainage are, therefore, argued to be a highly important control on surface evolution in ice-cored terrain, resulting in a diverse response of high-Arctic glacial landsystems to climatic amelioration. These results highlight that care is needed when assessing the long-term preservation potential of contemporary landforms at high-Arctic glaciers. A better understanding of ice-cored terrain facilitates the development of appropriate age and climatic interpretations that can be obtained from palaeo ice-marginal landsystems. [ABSTRACT FROM AUTHOR]

The digital reconstruction of buildings is a hot topic in many fields, such as archeology, architecture, civil engineering, computer vision, computer graphics, surveying, photogrammetry and many more. A variety of approaches has been developed and is currently used, in parallel and independent from each other. This paper will bridge the gap between architectural computer graphics using just photos and photogrammetry and laser scanning, which uses digital imagery to get high density coloured point clouds for 3D modelling. It starts with the workflow of laser scanning and photogrammetry and finally delivers 3D Virtual Reality models of buildings. On the other hand, those buildings can be augmented with 3D models reconstructed from old photos using architectural computer graphics to reconstruct the past. The validation and the automation of workflows brings together both disciplines, which will definitely benefit from each other. [ABSTRACT FROM AUTHOR]

Water erosion affects all types of soils around the world at different intensities. However, in the tropics, water-based processes are the most important of the erosion processes and have received much attention in the last decades. Understanding and quantifying the processes involved in each type of water erosion (sheet, rill and gully erosion) is key to developing and managing soil conservation and erosion mitigation strategies. This study aims to investigate the efficiency of unmanned aerial vehicle (UAV) structure-from-motion (SfM) photogrammetry for soil erosion assessment, as well as to address some gaps in our understanding of the evolution of erosive processes. For the first time, we used a UAV-SfM technique to evaluate the relative contribution of different types of erosion (sheet, rill and gully sidewall) in gully development. This was possible due to the millimetric level of precision of the point clouds produced, which allowed us to evaluate the contribution of laminar erosion as a new component to gullies studies. As a result, it was possible to quantify sediment volumes stored in the channels and lost from the gully system, as well as to determine the main sediment sources. The UAV-SfM proved to be effective for detailed gully monitoring, with the results suggesting that the main source of sediments in the gully was mass movement, followed by rills and sheet erosion. Our findings support the use of UAV-based photogrammetry as a sufficiently precise tool for detecting soil surface change, which can be used to assess water erosion in its various forms. In addition, UAV-SfM has proven to be a very useful technique for monitoring soil erosion over time, especially in hard-to-reach areas.

In this study, an approach using ground control point-free unmanned aerial vehicle (UAV)-based photogrammetry is proposed to estimate the volume of stockpiles carried on barges in a dynamic environment. Compared with similar studies regarding UAVs, an indirect absolute orientation based on the geometry of the vessel is used to establish a custom-built framework that can provide a unified reference instead of prerequisite ground control points (GCPs). To ensure sufficient overlap and reduce manual intervention, the stereo images are extracted from a UAV video for aerial triangulation. The region of interest is defined to exclude the area of water in all UAV images using a simple linear iterative clustering algorithm, which segments the UAV images into superpixels and helps to improve the accuracy of image matching. Structure-from-motion is used to recover three-dimensional geometry from the overlapping images without assistance of exterior parameters obtained from the airborne global positioning system and inertial measurement unit. Then, the semi-global matching algorithm is used to generate stockpile-covered and stockpile-free surface models. These models are oriented into a custom-built framework established by the known distance, such as the length and width of the vessel, and they do not require GCPs for coordinate transformation. Lastly, the volume of a stockpile is estimated by multiplying the height difference between the stockpile-covered and stockpile-free surface models by the size of the grid that is defined using the resolution of these models. Results show that a relatively small deviation of approximately ±2% between the volume estimated by UAV photogrammetry and the volume calculated by traditional manual measurement was obtained. Therefore, the proposed approach can be considered the better solution for the volume measurement of stockpiles carried on barges in a dynamic environment because UAV-based photogrammetry not only attains superior density and spatial object accuracy but also remarkably reduces data collection time.