The Rural Revitalization Strategy is a major initiative launched by China to address the emerging challenges facing agriculture and rural areas under new historical conditions. Geographical elements such as the human-environment relationship, resource distribution, and physical geography are closely linked to the all-round development of the countryside, providing a theoretical basis for the implementation of the rural revitalization strategy, while mapping geographic information data, as an important support for rural revitalization, plays an irreplaceable role in precise policymaking and scientific planning. Among various remote sensing observation means and earth observation instruments, unmanned aerial vehicle (UAV) technology has seen rapid advancement due to its high spatial resolution, flexibility, and cost-effectiveness, revealing significant potential for rural revitalization research. At EGU25, we will present the innovative applications of UAV data across multiple rural revitalization scenarios. Specifically, we will discuss the use of UAV data in rural industrial development, illustrated by marine aquaculture in coastal towns; the role of UAV data in preserving historical and cultural heritage; its contribution to urban-rural integration and governance in urban villages; and the promising future of UAV data in rural tourism development, including digital tourism and 3D navigation. Additionally, we will outline methods for processing multi-sensor UAV data and demonstrate how these results are integrated into practical platforms to foster rural development. We hope that this exchange will inspire broader applications of drone technology in rural development worldwide, contributing to the comprehensive and sustainable advancement of global rural areas.
How to cite: Kuang, J.: Innovative Applications of UAV Data in Rural Revitalization: Case Studies and Insights, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-588, https://doi.org/10.5194/egusphere-egu25-588, 2025.
The use of Uncrewed Aerial Vehicles (UAVs), including both autonomous and remotely piloted aerial systems, is increasingly prevalent across various scientific disciplines, enabling the collection of large volumes of data for diverse research applications. These data are essential for environmental monitoring, such as terrestrial and marine studies, species detection, and atmospheric data collection. However, the volume of data generated and the absence of standardised workflows often complicate data sharing and publication. To address these challenges, the Natural Environment Research Council (NERC) Environmental Data Service (EDS, [1]) has developed guidelines aimed at ensuring that UAV-collected data are Findable, Accessible, Interoperable, and Re-usable (FAIR) [2][3]. In collaboration with the Research Data Alliance, ongoing efforts are focused on developing recommendations for both general and domain-specific data formats and metadata, while also addressing challenges such as ethics and the use of persistent identifiers (PIDs) for instruments [4]. These efforts aim to streamline the data lifecycle for research using small UAVs and autonomous platforms, facilitating integration into research cloud infrastructures.
[1] https://eds.ukri.org/environmental-data-service
[2] Fremand, Alice. 2023 UAV data management handbook. UK Polar Data Centre, British Antarctic Survey, 13pp. https://nora.nerc.ac.uk/id/eprint/536392/
[3] Fremand, Alice. 2023 Towards a data commons: Imagery and derived data from autonomous and remotely piloted aerial vehicles. UK Polar Data Centre, British Antarctic Survey, 24pp. https://nora.nerc.ac.uk/id/eprint/536398/
[4] https://www.rd-alliance.org/groups/small-uncrewed-aircraft-and-autonomous-platforms-data-working-group/members/all-members/
How to cite: Fremand, A., Manthorpe, S., Whitelaw, M., Klump, J., and Semong, T.: Improving FAIRness of drone data through community effort, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1708, https://doi.org/10.5194/egusphere-egu25-1708, 2025.
In recent years, the frequency of extreme climate events has increased, and global warming has intensified. The growing occurrence and severity of such events have prompted nations to place greater emphasis on carbon emission reduction and environmental protection. Concurrently, the rapid depletion of traditional energy sources, coupled with rising global energy demand, has made the development of renewable energy a critical priority. Among the various renewable energy sources, solar photovoltaics (PV) has emerged as a highly promising solution due to its clean and efficient characteristics. As flat land resources become increasingly limited in certain regions, sloping terrains are increasingly being considered as viable sites for solar PV installations, owing to their favorable sunlight exposure and land utilization potential. However, the installation and maintenance of PV systems on slopes present several challenges, including low inspection efficiency, high operational costs, and risks to the structural stability of the systems, which are exacerbated by slope-specific geological hazards such as erosion. This study proposes an integrated approach for hotspot identification and erosion detection in photovoltaic (PV) systems installed on sloped terrains using drone technology. By utilizing drone-derived 3D terrain models, the study estimates actual solar illumination and detects disaster-inducing erosion gullies within potential PV installation zones, thereby facilitating the identification of optimal installation sites. The results of this research will serve as a critical reference for the deployment of PV systems in complex terrains, with particular emphasis on assessing the associated slope disaster risks.
How to cite: Kuo, Y. C., Lin, S. T., and Hsiao, Y. S.: Estimation of Photovoltaic Capacity and Detection of Gully Erosion based on Drone-generated 3D Terrain Models., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1818, https://doi.org/10.5194/egusphere-egu25-1818, 2025.
In recent years, the rise of the "digital twin" concept has significantly expanded the potential applications of 3D modeling in disaster prevention engineering. Among these, unmanned aerial vehicle (UAV) technology has emerged as a key method for terrain and structural modeling. As UAV technology continues to advance rapidly, its use in engineering has become increasingly widespread. With their high mobility and low-altitude operational capabilities, UAVs effectively overcome challenges such as post-disaster traffic disruptions and cloud cover. They are now widely utilized in tasks like terrain mapping, regional inspections, vegetation seeding, and disaster prevention surveys. Despite these advantages, UAV-based 3D modeling faces challenges. Vegetation often obstructs the clear capture of structures, and modeling resolution can be inadequate for certain applications. However, the LiDAR functionality integrated into smartphones offers a promising solution. This technology enables the rapid creation of high-precision 3D models and the accurate measurement of structural dimensions and volumes, even in steep terrains or remote locations that are difficult to access. This study explores the integration of UAV technology with ground-based LiDAR scanning using the iPhone 15 Pro, aiming to achieve seamless 3D modeling through an air-ground integration approach. The research focuses on merging aerial and ground point clouds. To accomplish this, we will employ both commercial software (e.g., Pix4Dmatic) and custom-developed point cloud fusion techniques. Each approach will be evaluated and compared comprehensively in terms of cost, processing time, and model accuracy. By combining UAV with smartphone-based applications, this study is expected to significantly improve model accuracy and detection efficiency in disaster prevention engineering. Moreover, the proposed approach holds broad potential for application in other fields, such as road design, landscape architecture, and urban planning.
How to cite: Lin, S. T., Kuo, Y. C., and Hsiao, Y. S.: Research on Seamless 3D Modeling Using UAV Technology Combined with Smartphone LiDAR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1821, https://doi.org/10.5194/egusphere-egu25-1821, 2025.
Although ultraviolet (UV) reflectance is linked to various environmental factors, it remains underutilized in remote sensing applications. This study explores the potential of UV-visible (UV-vis) reflectance for vegetation monitoring using unmanned aerial vehicles (UAVs). A UAV-mounted spectrometer was employed to collect point reflectance data across the study area, which was then georeferenced and interpolated to produce continuous reflectance images. The leaf area index (LAI) was used to illustrate the effectiveness of UV reflectance in vegetation monitoring. Our findings indicate strong agreement between UAV-derived reflectance images and Sentinel-2 data. Validation revealed that incorporating UV reflectance into LAI models alongside visible reflectance resulted in an R² improvement of up to 29.2% and an RMSE reduction of up to 18.9%, compared to models using only visible reflectance. This study demonstrates that UV reflectance measurements in the 320–400 nm range are feasible with UAV-based remote sensing and that hyperspectral UV-vis reflectance imaging offers significant value for vegetation monitoring. Additionally, the results suggest that refining our measurement system or performing experiments in a different environment could enable reflectance measurements at wavelengths as low as 290 nm.
How to cite: Berezowski, T., Kulawiak, M., and Kulawiak, M.: Hyperspectral UV-Vis Reflectance Imaging Using UAVs for Leaf Area Index Remote Sensing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3114, https://doi.org/10.5194/egusphere-egu25-3114, 2025.
A Digital Twin (DT) is an accurate virtual representation of a physical object, phenomenon, or process. DTs are utilized in geosciences to simulate and analyze complex terrains. Developing DTs of rock glaciers is essential due to their significant role in addressing challenges related to climate change, monitoring permafrost, water resource management, and the dynamics of mountain ecosystems. Creating a DT of a rock glacier is very challenging because of the monitoring strategy (regarding spatial, temporal, and spectral resolution), the selected tools for processing, analyzing, and simulating data collected, and the computational infrastructure.
In this work, we study the rock glacier of Lazaun, South Tyrol, Italy (southern Ötztal Alps). The actively moving rock glacier (elevation range 2,480 to 2,700 m a.s.l.; 0.12 km2) is a prominent site for long-term monitoring and research, being one of the most investigated rock glaciers worldwide in terms of internal structure, motion, and hydrological behaviour.
Here, we present a scale-down strategy starting from remote sensing products using differential synthetic aperture radar interferometry (DInSAR) to analyze extensive areas and identify active zones. Over these identified zones, we introduced a proximal sensing approach using drones equipped with specialized sensors.
Using high-resolution cameras, we captured and combined overlapping images through photogrammetry techniques to generate detailed orthomosaics, 3D models, and Digital Surface Models. Additionally, we incorporated thermal imaging from UAV sensors to detect land surface temperature variations, inspect the presence of subsurface ice, and identify areas of activity.
These data sources offer unparalleled spatial resolution and detail, which is crucial for building an accurate DT. Using GNSS to determine displacement and velocity, we continued a long-lasting in-situ method to measure the coordinates of specific features (boulders). We integrate ground photography to identify their shapes in drone products for further automatic shape identification.
Finally, we introduced the use of FLAC3D (Fast Lagrangian Analysis of Continua for 3D modeling) to understand the propagation and evolution of the rock glacier movement by using a viscous constitutive model whose parameters have been calibrated by matching the velocity field of the central part of the glacier. We propose the use of Azure Digital Twins tool to visualize the possible combinations of data and scenarios.
How to cite: Mejia-Aguilar, A., Dedivitiis, G., Crippa, C., Cuozzo, G., Delpero, M., Serban, R.-D., Brighenti, S., Marrazzo, G., and Calvetti, F.: Creating Digital Twins of Mountain Complex Terrains: A Study Case in the Lazaun Rock Glacier, South Tyrol, Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6139, https://doi.org/10.5194/egusphere-egu25-6139, 2025.
To increase the efficiency and accuracy of traditional hydraulic construction inspections, this study proposes an automated inspection and data management system using UAV and artificial intelligence (AI) technology. A 600-meter-long part of the river was chosen as a demonstration area, with emphasis on the use of bank condition study and deterioration identification. The approach consists of three major components. Initially, high-resolution photographs were obtained by drone once a month. Second, the YOLOv8 and Unet++ models were used to segment and detect damage in the photos. Finally, a data management platform was developed to allow for the systematic integration of picture data and the automated compilation of standardised inspection reports. The results indicate that the strategy considerably improves inspection efficiency and accuracy. The combination of uav and AI technology greatly reduces inspection time and successfully inspects a 600-meter radius of the bank. The model achieves a high IOU score and several damage detection indexes in berm segmentation, demonstrating the technical solution's practicality and application promise. This study confirms the potential use of UAV and AI technologies for hydraulic structural inspection, offering an efficient and data-driven inspection solution. Future research will focus on improving the AI model's performance, broadening the range of data samples, and supporting the complete implementation of intelligent monitoring and maintenance of water conservancy infrastructure.
How to cite: Kan, Y.-H., Jiang, G.-J., and Han, J.-Y.: UAV Mapping and AI Recognition for Inspection of Structural Defects in Water Resources, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7590, https://doi.org/10.5194/egusphere-egu25-7590, 2025.
Exploitation of ornamental rock from quarries requires innovative approaches that can provide sustainable access to construction materials. Within a quarry, rock types can vary significantly, and its assessment demands a detailed evaluation of its lithological variations and fracture network. Adequate resource management is critical to minimize product waste, improve process efficiency and increase its worth along the value chain.
Using Unmanned Aerial Vehicles (UAVs), operating at low level flights, equipped with high resolution RGB cameras, GPS and GNSS systems, in addition to real-time differential data (RTK) and GCP, image datasets were acquired to perform 4D reconstructions and interpret highly detailed morphologic and geologic features.
We report the results of rock typing obtained from Digital Elevation Models, Orthophotos, Point Clouds and Mesh datasets, such as fracture network characterization and cavity delineation, that provide essential information about penalizing features, mandatory for informed decision-making during exploitation. These include: 1) manual and automated fracture delineation; 2) rock typing; 3) optimization of the exploitation plan, 4) volumetric estimation of land and karst cavities; and 5) stock management.
We used segmentation techniques on point clouds to analyze structural discontinuities and identify faults and fractures at different scales, in the quarry extraction fronts. The applied algorithms enable the automatic extraction of geological planes and determine the geometric characteristics of the point cloud. The RGB color variation in the point clouds was also analyzed, enabling the delimitation of areas with different colors, which are generally associated with the degree of rock alteration. This analysis also allows the accurate detection of fracture networks. Results from image analysis allow individualising discrete types of rock and confidently extract fracture networks. The discrete features extracted from the models were subsequently validated with fieldwork at the quarries.
Acknowledgements: The authors thank Research Project PRR - Sustainable Stone by Portugal integrated on the Mobilizing Agenda and Fundação para a Ciência e a Tecnologia, I.P. (FCT), Portugal, through the research unit UIDB/04035/2020 - GeoBioTec.
How to cite: Kullberg, J., Pereira, R., Machadinho, A., and Santos, A.: 4D Digital Twin geological modelling for sustainable quarry management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17148, https://doi.org/10.5194/egusphere-egu25-17148, 2025.
Advances in UAV technology and data processing platforms have opened new possibilities for studying complex geophysical processes. Landslide dams, formed by large-scale landslides or debris flows, present significant hazards due to their potential for sudden breaching. While overtopping failures have been studied through various scales of experiments, seepage-induced failures remain less understood due to their inherently complex nature. Unlike overtopping, seepage-induced failures are characterized by unpredictable failure locations, internal structural weakening, and dynamic seepage progression, making real-time monitoring particularly difficult. Small-scale laboratory experiments are often inadequate for studying seepage-induced failures due to scaling effects and the inability to fully replicate real-world conditions. This study underscores the importance of conducting large-scale field experiments, where natural seepage processes can be observed in greater detail and under realistic conditions. We introduce an innovative UAV-based framework that employs multiple UAVs equipped with onboard cameras to simultaneously construct high-resolution digital elevation models (DEMs) and track particle motion in 3D to capture the flow field during large-scale seepage-induced dam failure experiments. By coordinating multiple UAVs and applying automated calibration of control points, the system achieves high-precision 3D surface modeling and velocity field extraction in real time. Preliminary results show that this UAV-based approach effectively captures critical seepage-driven structural weakening and internal collapse processes, providing a detailed 3D representation of flow dynamics. The developed methodology addresses key limitations of small-scale laboratory experiments and offers a scalable solution for investigating complex geohazard phenomena. It opens new opportunities for applying UAV technology in geohazard research, including hydrological studies, geomorphological investigations, and disaster mitigation.
How to cite: Hung, C.-Y., Su, Y.-T., Wang, H.-Y., and Chen, S.-C.: Utilizing UAVs for Simultaneous DEM Construction and 3D Velocity Tracking in Seepage-Induced Landslide Dam Failures, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18333, https://doi.org/10.5194/egusphere-egu25-18333, 2025.
In course of the Project ‘FlowCast’ (ESS Programme / Austrian Academy of Sciences, 2019-2023) a semi-airborne UAV frequency domain EM systems has been developed with the aim of achieving high penetration depths with a UAV sensor system. This required an innovative, powerful inductive source that, due to weight considerations, is designed to be stationary on the ground while the receiver is towed by a drone. Temporal synchronization of transmitter and receiver takes place via GPS time signal, relative geometry using GPS-RTK recording, correction of the orientation of the receiver using IMU module. In the shallow domain the semi-airborne UAV-EM configuration is compared to a full airborne UAV-EMI system. Both systems can be configured very flexibly in multi-frequency and multi-coil operation and can be optimized for various tasks (conductivity range in the subsurface, penetration depth, resolution). The study presents the hardware development (UAV sensor platform, transmitter, receiver) and data processing (preprocessing and inversion) as well as the results of case studies on test areas around Vienna with buried test bodies or natural structures and ongoing work.
An AIR8 medium lifter (Air6 Systems) octocopter with 25 kg total takeoff mass and 10 kg payload serves as the sensor platform. The receiver is an autonomous sensor system that measures and records amplitude and phase of the magnetic component of a low frequency electromagnetic (EM) signal that is emitted by the stationary transmitter. Auxiliary sensors acquire an accurate time tag, the sensor's GPS position, and the orientation of its main axis. The sensor has a mass of 5 kg, provides sufficient robustness for practical field operations, and can be either hand-positioned along a defined profile, or suspended from an airborne platform that positions the device at a defined position and orientation in 3D space. The combination of position-, amplitude- and phase information of both the receiver and the transmitter allow the estimation of sub-surface electrical conductivity during post-processing of recorded data.
The frequencies to be generated by the transmitter are in the audio range. Wire loop diameter ~ 30m, N=3. A Class D amplifier was developed that voltages up a chopped transmitter signal with a switching frequency of 195 kHz via a transformer and then reassembles it. An FPGA (Field Programmable Logic Array) is used to generate the pulse pattern and the sine signals. This creates a purely digital system. This solution enables large currents in the transmitter loop, thus large dipole moments over a wide frequency range. The 3D inversion program developed at KIGAM is based on the vector finite element method for the 3D electromagnetic forward modeling. The secondary field formulation is used to obtain the secondary field from primary field by ground loop source. 3D resistivity distribution is reconstructed from field EM responses using iterative least squares inversion method adopting active constraint balancing algorithm.
How to cite: Schiller, A., Kreuzer, G., Wolf, L., and Yi, M.-J.: Development and application of a multifrequency/multicoil semi airborne UAV-FDEM-system with optimized inductive source, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19518, https://doi.org/10.5194/egusphere-egu25-19518, 2025.
