Natural disasters and intensive land use increasingly threaten archaeological and cultural heritage (ACH) sites. To address this, the German Archaeological Institute (DAI), with the Leibniz Centre for Archaeology (LEIZA) and the Federal Agency for Technical Relief (THW), launched in 2021 the project KulturGutRetter (KGR), establishing a rapid-deployment Cultural Heritage Response Unit (CHRU) with the focus on safeguarding worldwide cultural heritage at risk after disasters. While the CHRU has not officially declared operational readiness yet, it was able to gain valuable experience during a national full-scale exercise and by participating in an EU MODEX (Module Exercise) in the Venice Lagoon (both in autumn 2024; e.g. Papa et al., 2025), which it has incorporated into its further development. Part of this experience also relates to the digital infrastructure, which CHRU relies on for efficient and uniform data collection during operations. Before, during and after missions, the CHRU employs fully open-source, standardised workflows for the documentation and assessment of affected cultural heritage, spanning the entire disaster-response chain. Before teams are dispatched, a remote-sensing and GIS (Geographic Information Systems) support group compiles the latest satellite imagery, augments it with the in-house “KGR Finder” archaeological tool, and produces layered risk and damage maps. These datasets, packed together with a standard data model, are loaded to handheld devices running mobile GIS software for immediate field use. On site, CHRU team members record damage through structured digital forms and, when conditions allow, they conduct UAV (Unmanned Aerial Vehicle) photogrammetry, laser scanning and other 3D survey methods. Moveable artefacts are registered on the spot: each object receives a QR-coded ID-card, enabling real-time assessment and tracking. A portable IT stack underpins this workflow. A mobile server hosts a PostgreSQL/PostGIS database, network-attached storage and synchronisation services, all accessible via an ad-hoc Wi-Fi or local cellular network. This lets teams consolidate data from multiple instruments instantly, secure it redundantly and keep every device in sync even in disconnected environments. After a mission, all remotely sensed and field data that are free of third-party restrictions are transferred to the host nation’s antiquities authorities as a structured package, then archived on DAI servers for future research and publication upon agreement. The field records also provide essential ground truth for refining satellite-based assessments. This paper outlines the open-source technologies, data standards and practical lessons that make CHRU deployments globally scalable, rapid and resilient, thus offering a blueprint for safeguarding ACH under growing disaster pressure. Highlights A general overview of all the digital components of the CHRU is given We illustrate the workflow of different components and how the data is gathered or recorded and further analysed Data management infrastructure is described, including both software and hardware perspectives The aim is to make a clear workflow of handling different datasets from the beginning to the end of a mission

Identifying discontinuities in high, steep rock slopes is challenging. This study proposes a high-precision geometric feature measurement method for discontinuities on the basis of point cloud data acquired via unmanned aerial vehicle (UAV) photography. The method effectively extracts key parameters, including orientation, trace length, and spacing. The implementation process comprises five main steps. First, principal component analysis (PCA) is used to extract feature information from the point cloud data. Second, the point cloud is preliminarily segmented via a curvature threshold and the density-based spatial clustering with noise (DBSCAN) algorithm. Third, the density peak clustering (DPC) algorithm is adopted to identify cluster centers and divide the discontinuity sets. Fourth, secondary DBSCAN clustering is performed on each discontinuity set to obtain complete individual discontinuities. Finally, geometric characteristics such as orientation, trace length, and spacing are measured on the basis of the principles of analytic geometry. The experimental results show that the orientation deviation calculated by this method is within an acceptable range and that the proposed method has higher computational efficiency than the traditional DPC method. The influence of random fracture networks (DFNs) on the stability of rock slopes was investigated via discrete element numerical simulations.

Revisiting slope-area thresholds for gully initiation: a systematic review and meta-analysis

This study proposes a feature-level fusion method that integrates Differential Interferometric Synthetic Aperture Radar (D-InSAR) and Unmanned Aerial Vehicle photogrammetry (UAV-P) for monitoring mining-induced subsidence basin (MSB). The method begins by extracting key subsidence characteristics based on the patterns of coal-mining-related surface displacement; the centimeter-level subsidence boundary is determined from D-InSAR data, while the meter-scale deformation at the subsidence center is derived from UAV-P. These extracted features are then used to invert the parameters of the probability integral method (PIM). The subsidence basin predicted by the inverted parameters serves as a criterion to select the superior dataset between the D-InSAR and UAV-derived results. Finally, the selected subsidence data are fused to generate a composite subsidence map. The proposed method was applied to the 2S201 panel in the Wangjiata Coal Mine using eight Sentinel-1A images and two UAV surveys. The fusion results were evaluated for their regional and overall accuracy against 30 ground control points measured by total station and GPS. The results demonstrate that the fusion method not only accurately extracts large-scale deformations in the mining area, with a maximum subsidence of 2.5 m and a root mean square error (RMSE) of 0.277 m in the subsidence center area, but also precisely identifies the subsidence boundary region with an accuracy of 0.039 m. The fused subsidence basin exhibits an overall accuracy of 0.182 m, which represents a significant improvement of 83.6% and 27.8% over the results obtained using D-InSAR and UAV alone, respectively. This method effectively reconstructs the complete morphology of the mining-induced subsidence basin, confirming its feasibility for practical applications.

Abstract An integrated framework for construction waste management is presented, leveraging unmanned aerial vehicle (UAV) photogrammetry, multi-altitude spatially linking sequential images imaging, 3D reconstruction, and an enhanced you only look once (YOLOv8) detection model with convolutional block attention module attention. UAV flights over a 0.79 km 2 construction area at 30 m, 60 m, and 100 m produced high-resolution orthomosaics, digital surface models, and multilayer 3D models that accurately captured waste clusters, building rooftops, machinery roads, natural features, and undeveloped land. The improved YOLOv8 configuration achieved a mean average precision (AP) of 0.934 at intersection over union 0.5 and an overall mean AP of 0.9498, surpassing YOLOv4, YOLOv5, and baseline YOLOv8 by up to 2.83%. Strong detection accuracy was obtained for poly bags (0.9491), plastic board and metal (0.9342), bricks and concrete (0.9513), and mixed waste (0.9647), supported by p -values above 0.99 for road and rooftop classes. The spatial outputs revealed the dominant waste distributions and enabled targeted recycling and disposal strategies. The framework provides a scalable, high-fidelity system for real-time construction waste detection and supports data-driven decision-making for sustainable site management.

Abstract. Digital access to archaeological and cultural heritage sites is increasingly important for research, conservation, and public dissemination. This paper presents a hybrid survey strategy combining Unmanned Aerial Vehicle (UAV) photogrammetry and Portable Laser Scanning (PLS) based on Simultaneous Localization and Mapping (SLAM) technology for the documentation of the Ain Akrine archaeological site (Koura region, northern Lebanon), a vegetated hilltop area hosting the Roman Qasr Naous temples. The proposed multi-sensor approach enables the generation of site-scale orthophotos and surface models, detailed photogrammetric reconstructions of the temples, and a SLAM-derived point cloud providing continuous coverage of the entire area, including densely vegetated zones. The integration of multiple PLS acquisitions with photogrammetric datasets results in a spatially consistent and multi-scale 3D representation. Quantitative accuracy analyses confirm the high reliability of the photogrammetric products and demonstrate that the SLAM-based survey achieves accuracy levels consistent with the expected performance of the adopted sensor, despite the absence of external constraints. The results highlight the effectiveness and flexibility of hybrid UAV–PLS approaches for comprehensive archaeological documentation under challenging environmental and logistical conditions.

The monitoring and conservation of built heritage is a major challenge for the scientific community, given the continuous degradation caused by natural, anthropogenic and climatic factors. The generation of high-resolution 3D documentation is important in the diagnosis of deterioration in historic buildings and the planning of conservation and restoration efforts. The present study proposes an integrated, multi-source workflow combining terrestrial laser scanning (TLS), unmanned aerial vehicle (UAV) photogrammetry, and 3D camera interior scanning. This workflow was employed to document and evaluate the Casa Rusănescu monument in Craiova, Romania. The following processes were incorporated: coordinated acquisition, processing, alignment, evaluation of geometric consistency and deviation-based diagnosis. The diagnosis process include measuring the distance between data clouds and analyzing surface roughness, curvature, planarity and linearity. The workflow was designed to be applicable in real urban conditions, ensuring the coverage of façades, interiors and roof structures. The final, combined dataset contained over 235 million points and includes both interior and exterior geometries. This process helped identify various types of damage, such as cracks, exfoliation, plaster detachment, moisture-related changes, and geometric deformations. An additional AI-assisted validation step (Twinspect) was used to cross-check the degradation indicators derived from point-cloud analyses. The findings suggest that using multiple sensors improves spatial completeness, enhances anomaly detection, and establishes a reliable baseline prior to restoration interventions and long-term monitoring. This methodology facilitates the development of digital twins and GIS-based risk assessments, thereby providing a scalable solution for heritage preservation.

Incomplete survey data often undermines the reliability of Building Information Models (BIM), particularly for structures with restricted access and complex geometries. This study demonstrates a hybrid Scan-to-BIM workflow that integrates terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry, supported by a predictive feasibility concept, to improve documentation accuracy and completeness. A two-phase strategy was validated on a chapel case study. Phase 1, combining TLS and ground-based photogrammetry, achieved only 54% coverage due to severe occlusions and limited scanner placement. These results led to the formulation of a Predictive Scan Feasibility Estimation Model (PSFEM), designed to generalize site-specific parameters such as scanner range, clearance angle, and building height into a decision-support tool for future surveys. Guided by the recognition of Phase 1 limitations, Phase 2 incorporated UAV photogrammetry and supplemental TLS, increasing coverage to 96%. Comparative analyses confirmed consistency in accuracy and improved geometric completeness. While the PSFEM was developed retrospectively based on the limitations identified in Phase 1, its analytical validation demonstrates the potential value of predictive planning for reducing redundant site visits and enhancing BIM reliability. The proposed framework provides a transferable basis for applying predictive hybrid workflows in both heritage and complex building documentation. This workflow offers a practical and scalable method for Scan-to-BIM documentation, applicable to heritage as well as other complex buildings, enabling high accuracy and completeness while effectively managing time and resources.

Accurate characterization of river channel geometry is essential for hydrological and hydraulic analyses, yet the increasing use of unmanned aerial vehicle (UAV) photogrammetry introduces challenges related to uneven point density, shadow-induced data gaps, and spurious outliers. This study proposed a novel approach for reconstructing 3D river channels from UAV-derived point clouds, emphasizing K-nearest neighbor local regression (KLR), and compared it with the LOWESS model. Method performance was examined through controlled simulations of trapezoidal, triangular, and U-shaped synthetic channels, where KLR consistently preserved morphological fidelity and produced lower RMSE than LOWESS, particularly at channel bends and bed undulations, while a neighborhood selection heuristic approach demonstrated robust results across varying data densities. Synthetic channel experiments show that the proposed K-nearest-neighbor local linear regression (KLR) method achieves RMSE values below 0.06 all tested geometries. In contrast, LOWESS produces substantially larger errors, with RMSE values exceeding 0.9 across all channel shapes. Subsequent application to two South Korean field sites reinforced these findings. In the data-scarce Migok-cheon stream, KLR effectively interpolated missing surfaces while maintaining geomorphic realism, whereas LOWESS generated over-smoothed representations. Within the dense Ogsan Bridge dataset, KLR retained small-scale bed features critical for hydraulic simulations and cross-sectional delineation, while LOWESS obscured local variability. Conclusively, the results demonstrate that KLR provides a more reliable and computationally efficient framework for UAV-based 3D river channel reconstruction, with clear implications for hydraulic modeling, flood risk management, and the advancement of digital-twin systems in operational hydrology.

Accurate path planning was the prerequisite for autonomous navigation of agricultural vehicles. An Unmanned Aerial Vehicle (UAV)-based coverage path planning was developed in this research for automating guidance of agricultural vehicles and reducing the operator maneuver in the creation of navigation maps. High-resolution orthophoto maps of the field were constructed by using low-altitude UAV photogrammetry to obtain spatial information. Travel paths and working paths were automatically generated from anchor points selected by the operator under the image coordinate domain. The navigation path for unmanned agricultural vehicles was generated by Mercator projection-based conversion for the anchor pixel coordinates into latitude and longitude geographic coordinates. A Graphical User Interface (GUI) was developed for path generation, visualization, and performance evaluation, through which the proposed path planning method was implemented for autonomous agricultural vehicle navigation. Calculation accuracy tests demonstrated the mean planar coordinate error was 2.23 cm and the maximum error was 3.37 cm for path planning. Field tests showed that lateral navigation errors remained within ±5.5 cm for the unmanned high-clearance sprayer, which indicated that the developed UAV-based coverage path planning method was feasible and featured high accuracy. It provided an effective solution for achieving fully autonomous agricultural vehicle operations.

Hydroclimatic and cryospheric changes in the eastern Pamir Plateau, Tajikistan: A 30-a remote sensing assessment of Yashilkul Lake

Rockfall, a typical kinematic process near slope surfaces, poses challenges due to its high energy, unpredictability, and dependence on both analytical methods and topographic accuracy. In this study, the classical three-dimensional discontinuous deformation analysis (3D-DDA) is reformulated using cone complementary theory to better capture nonlinear contact interactions. Unmanned aerial vehicle (UAV) photogrammetry is employed to construct an accurate numerical model of the complex slope terrain at the No. 2 transverse tunnel of the Layue Tunnel along the Sichuan–Tibet Railway. Simulations reveal that dangerous rock masses primarily fail by sliding, with both isolated boulders and massive rock formations exhibiting diverse 3D motion patterns. The rockfalls traverse the tunnel construction zone, threatening traffic and river safety before deposition. UAV-assisted 3D-DDA effectively characterizes trajectories, displacements, and kinetic energy, enhancing prediction of impact zones and deposition sites. These results provide insights into rockfall mechanisms and support hazard mitigation strategies.

Mudstone badlands are critical hotspots of erosion and sediment yield, and their rapid morphological changes serve as an ideal site for studying erosion processes. This study used high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry to monitor erosion patterns on a mudstone badland platform in southwestern Taiwan over a 22-month period. Five UAV surveys conducted between 2017 and 2018 were processed using Structure-from-Motion photogrammetry to generate time-series digital surface models (DSMs). Topographic changes were quantified using DSMs of Difference (DoD). The results reveal intense surface lowering, with a mean erosion depth of 34.2 cm, equivalent to an average erosion rate of 18.7 cm yr−1. Erosion is governed by a synergistic regime in which diffuse rain splash acts as the dominant background process, accounting for approximately 53% of total erosion, while concentrated flow drives localized gully incision. Morphometric analysis shows that erosion depth increases nonlinearly with slope, consistent with threshold hillslope behavior, but exhibits little dependence on the contributing area. Plan and profile curvature further influence the spatial distribution of erosion, with enhanced erosion on both strongly concave and convex surfaces relative to near-linear slopes. The gully network also exhibits rapid channel adjustment, including downstream meander migration and associated lateral bank erosion. These findings highlight the complex interactions among hillslope processes, gully dynamics, and base-level controls that govern badland landscape evolution and have important implications for erosion modeling and watershed management in high-intensity rainfall environments.

The repeated extraction of coal seams in the Loess Plateau mining region has greatly increased the severity of surface deformation and associated damage. Accurately characterizing the spatio-temporal evolution of subsidence and the underlying mechanisms is a critical engineering challenge for mining safety. Taking the Dafosi Coal Mine located in the loess gully region as a case study, this paper thoroughly examines the variations in surface deformation and damage characteristics caused by single and repeated seam mining. The analysis integrates surface movement monitoring data, global navigation satellite system (GNSS) dynamic observations, field surveys, unmanned aerial vehicle (UAV) photogrammetry, and numerical simulation methods. Notably, to ensure the accuracy of prediction parameters, a refined Particle Swarm Optimization (PSO) algorithm incorporating a neighborhood-based mechanism was employed specifically for the inversion of probability integral parameters. The results indicate that the subsidence factor and horizontal movement factor increase markedly following repeated mining. The maximum surface subsidence velocity also increases substantially, and this acceleration remains evident after normalizing by mining thickness and face-advance rate. The fore effective angle is smaller in repeated mining than in single-seam mining, and the duration of surface movement is substantially extended. Repeated mining fractured key strata and caused a functional transition from the classic three-zone response to a two-zone connectivity pattern, while the thick loess cover responds as a disturbed discontinuous-deformation layer, which together aggravates step-like and slope-related damage. The severity of surface damage is strongly influenced by topographic features and geotechnical properties. These findings demonstrate that the proposed integrated approach is highly effective for geological hazard assessment and provides a practical reference for engineering applications in similar complex terrains.

The rapid growth of urban areas has increased the need for more efficient methods of pavement inspection and maintenance. However, conventional techniques remain slow, labor-intensive, and limited in spatial coverage, and their performance is strongly affected by traffic, weather conditions, and operational constraints. In response to these challenges, it is essential to synthesize the technological advances that improve inspection efficiency, coverage, and data quality compared to traditional approaches. Herein, we present a systematic review of the state of the art on the use of unmanned aerial vehicles (UAVs) for monitoring and assessing pavement deterioration, highlighting as a key contribution the comparative integration of sensors (photogrammetry, LiDAR, and thermography) with recent automatic damage-detection algorithms. A structured review methodology was applied, including the search, selection, and critical analysis of specialized studies on UAV-based pavement evaluation. The results indicate that UAV photogrammetry can achieve sub-centimeter accuracy (<1 cm) in 3D reconstructions, LiDAR systems can improve deformation detection by up to 35%, and AI-based algorithms can increase crack-identification accuracy by 10% to 25% compared with manual methods. Finally, the synthesis shows that multi-sensor integration and digital twins offer strong potential to enhance predictive maintenance and support the transition towards smarter and more sustainable urban infrastructure management strategies.

Southern right whales (Eubalaena australis; SRWs) are well adapted to cold waters because of their large body size and thick blubber. Each year, they migrate from high-latitude feeding grounds to warmer breeding grounds where they give birth. To assess thermal benefits of this migration, we modelled the effects of body size, condition and water temperature on heat loss. Using unmanned aerial vehicle photogrammetry at the Península Valdés calving ground in Argentina, we measured body length, volume, condition and surface area of living SRWs. Blubber thickness was predicted from a blubber-mass model and validated using necropsy/catch data. Sensible heat loss was estimated using a model incorporating blubber thermal conductivity and body temperature, whereas respiratory heat loss was based on respiration rate and tidal volume models. We compared heat loss in Península Valdés with that in South Georgia/Georgia del Sur (SG/GS), a key feeding ground. Body size had a strong positive effect on both heat loss values, but mass-specific loss decreased as surface-area-to-volume ratio declined. Increased body condition reduced sensible heat loss. Migration from SG/GS to Península Valdés reduced calf heat loss by 26% during early lactation. However, total heat loss remained low relative to field metabolic rate (FMR), indicating limited thermoenergetic benefit from migration. Only at poor body condition (<-0.35) did heat loss exceed FMR, threatening survival. Notably, gull-inflicted lesions significantly increased heat loss in small and poorly conditioned calves, but had no effect on larger or better-conditioned calves. These findings highlight body condition as a key regulator of heat loss in baleen whales.

Purpose. To determine the optimal number and spatial distribution of Ground Control Points (GCPs) required to achieve high-precision georeferencing in unmanned aerial vehicle (UAV) imagery, particularly for applications requiring 1:1,000 scale mapping. Methodology. Seven experimental scenarios were conducted, varying the number of GCPs from 4 to 30. For each scenario, GCPs were arranged in seven different spatial configurations, including central, corner, edge, and evenly distributed placements. The Root Mean Squared Error (RMSE) was calculated for each configuration to assess georeferencing accuracy. Findings. The results showed that using only 4 GCPs produced the highest RMSE, indicating the lowest accuracy. RMSE values decreased as the number of GCPs increased, with minimal improvement beyond 20 GCPs. Among all distribution patterns, placing GCPs at the corners consistently resulted in the highest RMSE. The most accurate results were achieved with 20 evenly distributed GCPs. Originality. This study provides a systematic evaluation of both the quantity and spatial arrangement of GCPs in UAV photogrammetry, offering empirical evidence to support optimal GCP deployment strategies. Practical value. The findings offer practical guidance for UAV mapping professionals, suggesting that 20 evenly distributed GCPs are sufficient to meet the accuracy standards for 1:1,000 scale maps. This helps optimize fieldwork efficiency while ensuring data quality.

Activities conducted using UAV photogrammetry in archaeology have become increasingly common in recent years as they simplify documentation in archaeological excavations and surface surveys while providing a new perspective. In this study, Digital Elevation Models, Orthophotos, and 3D Models were produced using Unmanned Aerial Vehicle (UAV) photogrammetry at the ancient city of Lystra, generating detailed topographic products for pre-excavation preparation phases. Thanks to UAV technology, archaeological remains on the surface were documented through three-dimensional models and high-resolution orthophotos. This method not only saves time in excavation planning but also significantly reduces costs and enhances the accuracy of archaeological data. This study highlights how effectively technology can be utilized in excavation planning, the analysis of surface remains, and the documentation of cultural heritage. Future research suggests the integration of different sensors for more comprehensive analyses.

ABSTRACT This study presents a comprehensive evaluation of unmanned aerial vehicle (UAV)‐assisted photogrammetry in documenting an archaeological reserve area believed to be part of the ancient settlement of Caesarea Germanicia, currently surrounded by contemporary urban fabric and dense vegetation. The assessment focuses on six key criteria: accessibility and limitations, data density, time efficiency, output quality, spatial accuracy and cost. The research explores whether UAV technology can outperform conventional ground‐based documentation methods—such as total station and manual surveying—across these key criteria. Low‐altitude flights conducted with a rotary‐wing UAV produced high‐resolution imagery at 1.96 mm/pixel, which was processed into orthophotos and three‐dimensional models (3D). These datasets effectively captured the site's topography, architectural layout and micro‐scale surface traces in high detail. Despite obstructions caused by modern structures and dense vegetation, submetric spatial accuracy was achieved without the use of ground control points (GCPs), utilizing GNSS‐based georeferencing. Compared to traditional systems, which often face limitations in setup, line of sight and site access, UAV photogrammetry enabled fast, low‐cost, noninvasive and high‐quality data acquisition. The findings indicate that UAV‐assisted photogrammetry should not merely be regarded as a technical alternative, but—under specific field conditions—as a primary and superior method for the documentation and preservation of cultural heritage sites.

Floods increasingly threaten semiarid regions, yet their long-term soil ecological impacts remain underdocumented. This study quantifies the hydrologic change and flood-induced soil transformation on the Zhabay River floodplain (Akmola, Kazakhstan) using integrated field, laboratory, and remote sensing data. Gauge records (2012–2024) were analyzed; inundation was mapped from a 0.30 m DEM (Digital Elevation Model) merging SRTM (Shuttle Radar Topography Mission), Landsat 8/Sentinel 2, and UAV (Unmanned Aerial Vehicle) photogrammetry (NDWI (Normalized Difference Water Index) &gt; 0.28) and validated with 54 in situ depths (MAE (Mean Absolute Error) 0.17 m). Soil samples collected before and after floods were analyzed for texture, bulk density, pH, Eh, macronutrients, and heavy metals. Annual maxima increased by 0.08 m yr−1, while extreme floods became more frequent. Thresholds of ≥0.5 m depth and &gt;7 days duration marked compaction onset, whereas &gt;1 m and ≥12 days produced maximum organic carbon loss and Zn/Ni enrichment. The combination of high-resolution DEMs, ROC (Receiver Operating Characteristic) analysis, and soil microbial monitoring provides new operational indicators of soil degradation for Central Asian steppe floodplains. Findings contribute to SDG 13 (Climate Action) and SDG 15 (Life on Land) by linking flood resilience assessment with sustainable land-use planning.