Search Results (236)

The accurate and large-scale mapping of seagrass meadows is essential, as these meadows form primary habitats for marine organisms and large sinks for blue carbon. Image data available for mapping these habitats are often scarce or are acquired through multiple surveys and instruments, resulting in images of varying spatial and spectral characteristics. This study presents an unsupervised domain adaptation (UDA) strategy that combines histogram-matching with the transformer-based SegFormer model to address these challenges. Unoccupied aerial vehicle (UAV)-derived imagery (3-cm resolution) was used for training, while orthophotos from airplane surveys (12.5-cm resolution) served as the target domain. The method was evaluated across three Danish estuaries (Horsens Fjord, Skive Fjord, and Lovns Broad) using one-to-one, leave-one-out, and all-to-one histogram matching strategies. The highest performance was observed at Skive Fjord, achieving an F1-score/IoU = 0.52/0.48 for the leave-one-out test, corresponding to 68% of the benchmark model that was trained on both domains. These results demonstrate the potential of this lightweight UDA approach to generalization across spatial, temporal, and resolution domains, enabling the cost-effective and scalable mapping of submerged vegetation in data-scarce environments. This study also sheds light on contrast as a significant property of target domains that impacts image segmentation. Full article

To address the operational demands of irregular farmland with fixed obstacles, this study proposes a full-coverage path planning framework that integrates UAV-based 3D perception and angle-adaptive optimization. First, digital orthophoto maps (DOMs) and digital elevation models (DEMs) were reconstructed from low-altitude aerial imagery. The feasible working region was constructed by shrinking field boundaries inward and dilating obstacle boundaries outward. This ensured sufficient safety margins for machinery operation. Next, segmentation angles were scanned from 0° to 180° to minimize the number and irregularity of sub-regions; then a two-level simulation search was performed over 0° to 360° to optimize the working direction for each sub-region. For each sub-region, the optimal working direction was selected based on four criteria: the number of turns, travel distance, coverage redundancy, and planning time. Between sub-regions, a closed-loop interconnection path was generated using eight-directional A* search combined with polyline simplification, arc fitting, Chaikin subdivision, and B-spline smoothing. Simulation results showed that a 78° segmentation yielded four regular sub-regions, achieving 99.97% coverage while reducing the number of turns, travel distance, and planning time by up to 70.42%, 23.17%, and 85.6%. This framework accounts for field heterogeneity and turning radius constraints, effectively mitigating path redundancy in conventional fixed-angle methods. This framework enables general deployment in agricultural field operations and facilitates extensions toward collaborative and energy-optimized task planning. Full article

In the case of forest inventory, heterogeneous areas are particularly challenging due to variability in vegetation structure. This is especially true in Galicia (northwest Spain), where land is highly fragmented, complicating the planning and management of single-species plantations such as Pinus radiata. This study proposes a cost-effective strategy using open-access tools and data to characterize and estimate wood volume in these plantations. Two stratification approaches—classical and cluster-based—were compared to a modeling method based on Principal Component Analysis (PCA). Data came from open-access national LiDAR point clouds, acquired using manned aerial vehicles under the Spanish National Aerial Orthophoto Plan (PNOA). Moreover, two volume estimation methods were applied: one from the Xunta de Galicia (XdG) and another from Spain’s central administration (4IFN). A Generalized Linear Model (GLM) was also fitted using PCA-derived variables with logarithmic transformation. The results show that although overall volume estimates are similar across methods, cluster-based stratification yielded significantly lower absolute errors per hectare (XdG: 28.04 m³/ha vs. 44.07 m³/ha; 4IFN: 25.64 m³/ha vs. 38.22 m³/ha), improving accuracy by 7% over classical stratification. Moreover, it does not require precise field parcel locations, unlike PCA modeling. Both official volume estimation methods tended to overestimate stock by about 10% compared to PCA. These results confirm that clustering offers a practical, low-cost alternative that improves estimation accuracy by up to 18 m³/ha in fragmented forest landscapes. Full article

In the production of a digital orthophoto map (DOM) from unmanned aerial vehicle (UAV)-acquired overlapping images, some anomalies such as texture stretching or data holes frequently occur in water areas due to the lack of significant textural features. These anomalies seriously affect the visual quality and data integrity of the resulting DOMs. In this study, we attempted to eliminate the water surface anomalies in an example DOM via replacing the entire water area with an intact one that was clipped out from one single UAV image. The water surface scope and boundary in the image was first precisely achieved using the multisource seed filling algorithm and contour-finding algorithm. Next, the tie points were selected from the boundaries of the normal and anomalous water surfaces, and employed to realize their spatial alignment using affine plane coordinate transformation. Finally, the normal water surface was overlaid onto the DOM to replace the corresponding anomalous water surface. The restored water area had good visual effect in terms of spectral consistency, and the texture transition with the surrounding environment was also sufficiently natural. According to the standard deviations and mean values of RGB pixels, the quality of the restored DOM was greatly improved in comparison with the original one. These demonstrated that the proposed method had a sound performance in restoring abnormal water surfaces in a DOM, especially for scenarios where the water surface area is relatively small and can be contained in a single UAV image. Full article

Search and rescue (SAR) missions require reliable search methods to locate survivors, especially in challenging environments. Introducing unmanned aerial vehicles (UAVs) can enhance the efficiency of SAR missions while simultaneously increasing the safety of everyone involved. Motivated by this, we experiment with autonomous UAV search for humans in Mediterranean karst environment. The UAVs are directed using the Heat equation-driven area coverage (HEDAC) ergodic control method based on known probability density and detection function. The sensing framework consists of a probabilistic search model, motion control system, and object detection enabling to calculate the target’s detection probability. This paper focuses on the experimental validation of the proposed sensing framework. The uniform probability density, achieved by assigning suitable tasks to 78 volunteers, ensures the even probability of finding targets. The detection model is based on the You Only Look Once (YOLO) model trained on a previously collected orthophoto image database. The experimental search is carefully planned and conducted, while recording as many parameters as possible. The thorough analysis includes the motion control system, object detection, and search validation. The assessment of the detection and search performance strongly indicates that the detection model in the UAV control algorithm is aligned with real-world results. Full article

In this study, we selected a series of pothole wetlands to investigate their nucleation, evolution, and recent anthropogenic degradation in the Alcores Depression (AD), southern Iberian Peninsula, where over 100 closed watersheds containing shallow, ephemeral water bodies up to 2 hm² have been identified. We surveyed the regional geological framework, utilized digital elevation models (DEMs), orthophotos, and aerial images since 1956. Moreover, we analyzed precipitation and temperature data in Seville from 1900 to 2024, collected hydrometeorological data since 1990 and modelled the water level evolution from 2002 to 2025 in a representative pothole in the area. Our observations indicate a flooded surface reduction by more than 90% from the 1950s to 2025. Climatic data reveal an increase in annual mean temperatures since 1960 and a sharp decline in annual precipitation since 2000. The AD’s inception due to tectonic isolation during the Quaternary favoured the formation of pothole wetlands in the floodplain. The reduction in the hydroperiod and wetland degradation was primarily due to agricultural expansion since 1950, which followed an increase in groundwater extraction and altered the original topography. Recently, decreased precipitation has exponentially accelerated the degradation and even the complete disappearance of many potholes. This study underscores the fragility of small wetlands in the Mediterranean basin and the critical role of human management in their preservation. Restoring these ecosystems could be a highly effective nature-based solution, especially in semi-arid climates like southern Spain. These prairie potholes are crucial for enhancing groundwater recharge, which is vital for maintaining water availability in regions with limited precipitation. By facilitating rainwater infiltration into the aquifer, recharge potholes increase groundwater levels. Additionally, they capture and store run-off during heavy rainfall, reducing the risk of flooding and soil erosion. Beyond their hydrological functions, these wetlands provide habitats that support biodiversity and promote ecological resilience, reinforcing the need for their protection and recovery. Full article

Surface cracks are key indicators of landslide deformation, crucial for early landslide identification and deformation pattern analysis. However, due to the complex terrain and landslide extent, manual surveys or traditional digital image processing often face challenges with efficiency, precision, and interference susceptibility in detecting these cracks. Therefore, this study proposes a comprehensive automated pipeline to enhance the efficiency and accuracy of landslide surface crack detection. First, high-resolution images of landslide areas are collected using unmanned aerial vehicles (UAVs) to generate a digital orthophoto map (DOM). Subsequently, building upon the U-Net architecture, an improved encoder–decoder semantic segmentation network (IEDSSNet) was proposed to segment surface cracks from the images with complex backgrounds. The model enhances the extraction of crack features by integrating residual blocks and attention mechanisms within the encoder. Additionally, it incorporates multi-scale skip connections and channel-wise cross attention modules in the decoder to improve feature reconstruction capabilities. Finally, post-processing techniques such as morphological operations and dimension measurements were applied to crack masks to generate crack inventories. The proposed method was validated using data from the Heifangtai loess landslide in Gansu Province. Results demonstrate its superiority over current state-of-the-art semantic segmentation networks and open-source crack detection networks, achieving F1 scores and IOU of 82.11% and 69.65%, respectively—representing improvements of 3.31% and 4.63% over the baseline U-Net model. Furthermore, it maintained optimal performance with demonstrated generalization capability under varying illumination conditions. In this area, a total of 1658 surface cracks were detected and cataloged, achieving an accuracy of 85.22%. The method proposed in this study demonstrates strong performance in detecting surface cracks in landslide areas, providing essential data for landslide monitoring, early warning systems, and mitigation strategies. Full article

Accurate localization of buried water pipelines in rural areas is crucial for maintenance and leak management but is often hindered by outdated maps and the limitations of traditional geophysical methods. This study aimed to develop and validate a multi-source remote-sensing workflow, integrating UAV (unmanned aerial vehicle)-borne near-infrared (NIR) surveys, multi-temporal Sentinel-2 imagery, and historical Google Earth orthophotos to precisely map pipeline locations and establish a surface baseline for future monitoring. Each dataset was processed within a unified least-squares framework to delineate pipeline axes from surface anomalies (vegetation stress, soil discoloration, and proxies) and rigorously quantify positional uncertainty, with findings validated against RTK-GNSS (Real-Time Kinematic—Global Navigation Satellite System) surveys of an excavated trench. The combined approach yielded sub-meter accuracy (±0.3 m) with UAV data, meter-scale precision (≈±1 m) with Google Earth, and precision up to several meters (±13.0 m) with Sentinel-2, significantly improving upon inaccurate legacy maps (up to a 300 m divergence) and successfully guiding excavation to locate a pipeline segment. The methodology demonstrated seasonal variability in detection capabilities, with optimal UAV-based identification occurring during early-vegetation growth phases (NDVI, Normalized Difference Vegetation Index ≈ 0.30–0.45) and post-harvest periods. A Sentinel-2 analysis of 221 cloud-free scenes revealed persistent soil discoloration patterns spanning 15–30 m in width, while Google Earth historical imagery provided crucial bridging data with intermediate spatial and temporal resolution. Ground-truth validation confirmed the pipeline location within 0.4 m of the Google Earth-derived position. This integrated, cost-effective workflow provides a transferable methodology for enhanced pipeline mapping and establishes a vital baseline of surface signatures, enabling more effective future monitoring and proactive maintenance to detect leaks or structural failures. This methodology is particularly valuable for water utility companies, municipal infrastructure managers, consulting engineers specializing in buried utilities, and remote-sensing practitioners working in pipeline detection and monitoring applications. Full article

The 1992 reform of the European Common Agricultural Policy (CAP) introduced the Land Parcel Identification System (LPIS), a geodatabase of land parcels used to monitor and regulate agricultural subsidies. Traditionally, the LPIS has relied on high-resolution aerial orthophotos; however, recent advancements in very-high-resolution (VHR) satellite imagery present new opportunities to enhance its effectiveness. This study explores the feasibility of utilizing PlanetScope, a commercial VHR optical satellite constellation, to map agricultural parcels within the LPIS. A test was conducted in Umbria, Italy, integrating existing datasets with a series of PlanetScope images from 2023. A segmentation workflow was designed, employing the Normalized difference Vegetation Index (NDVI) alongside the Edge segmentation method with varying sensitivity thresholds. An accuracy evaluation based on geometric metrics, comparing detected parcels with cadastral references, revealed that a 30% scale threshold yielded the most reliable results, achieving an accuracy rate of 83.3%. The results indicate that the short revisit time of PlanetScope compensates for its lower spatial resolution compared to traditional orthophotos, allowing accurate delineation of parcels. However, challenges remain in automating parcel matching and integrating alternative methods for accuracy assessment. Further research should focus on refining segmentation parameters and optimizing PlanetScope’s temporal and spectral resolution to strengthen LPIS performance, ultimately fostering more sustainable and data-driven agricultural management. Full article

Research on the dynamic mechanisms driving the accumulation of anthropogenic marine debris (AMD) in highly dynamic environments, such as extensive sandy beaches, remains limited. Unmanned aerial vehicles (UAVs) can be used to map macro-marine litter in these environments over large temporal and spatial scales, but several challenges remain in their interpretation. In this study, secondary school students participated in a citizen science initiative, during which they identified, marked, and classified waste items using a series of UAV orthophotos collected along an 800 m extended Italian beach in different seasons. A specific training program and a collection of working tools were developed to support these activities, which were carried out under the constraints imposed by the COVID-19 pandemic. The accuracy of the citizen science approach was evaluated by comparing its results with standard in situ visual census surveys conducted in the same area. This methodology not only enabled an analysis of the temporal dynamics of AMD accumulation but also served an important educational function. The effectiveness of the learning experience was estimated using pre- and post-activity questionnaires. The results indicate a clear improvement in the students’ knowledge, interest, and awareness regarding marine litter, highlighting the potential of citizen science to both support environmental monitoring and promote sustainability education among younger generations. Full article

With the increasing mining depth, the stability of open-pit mine slopes has become an increasingly important concern. This study focuses on an open-pit mine in Southwest China and utilizes unmanned aerial vehicle (UAV) technology to gather data from these high and steep slopes. First, high-precision digital surface models and digital orthophoto data are collected using UAV terrain-following flight technology. However, two major challenges arise when applying geographic information systems (GISs) to this issue. The first challenge is that the extreme steepness of the slopes causes overlapping lithological layers at the same location, which GISs cannot resolve. The second challenge is that GISs cannot assess the influence of faults on landslides by calculating three-dimensional spatial distances. To overcome these issues, this study proposes the construction of a detailed 3D geological model for the entire mining area. This model allows for a more precise analysis of the lithology and fault spatial distances. A GIS is then applied to analyze the slope, curvature, and slope direction. Multi-source data fusion is employed to link spatial coordinates and create a dataset for further analysis. Five machine learning models for landslide prediction are compared using this dataset. Based on these comparisons, a high-precision random forest and slope boosting coupled method is developed to enhance the landslide prediction accuracy. Finally, a numerical simulation of a regional focus area is conducted, simulating the excavation process of an open-pit mine and analyzing the timing, location, and state of potential landslides. The results indicate that combining machine learning and multi-source data fusion provides a highly accurate, efficient, and straightforward method for landslide prediction in the high and steep slopes of open-pit mines. Full article

Effective response to flood events requires high-resolution, frequently updated data on flooded areas for comprehensive flood risk assessments. Unmanned aerial vehicles (UAVs) equipped with conventional camera systems and classification based on orthophotos from photogrammetric postprocessing and artificial intelligence are widely used to detect flooded areas. However, these methods often involve time-intensive pre- and postprocessing steps and fail to incorporate geometric factors such as elevation data and water depths. This study introduces SSegRef2Surf, a novel tool that integrates classified flood raster data with terrain information. SSegRef2Surf refines and optimizes coarse raster classifications by filling shadowed areas and correcting misclassified regions. This tool reduces data requirements for AI training and minimizes postprocessing time, enabling near real-time flood monitoring. All processes necessary for SSegRef2Surf were optimized through sensitivity and accuracy analyses to reduce postprocessing duration to a minimum. A comparison of the SSegRef2Surf results with two-dimensional (2D) numerical model results for a flood event revealed discrepancies in the 2D model, caused by inaccuracies in the underlying terrain data. This comparison showed that 30% of the flooded areas identified in the 2D numerical results were incorrect, while missing areas (11%) were added. This highlights the significant potential of SSegRef2Surf for near real-time flood monitoring and traceability of flood events, as combining UAVs’ high-frequency surveying capabilities with SSegRef2Surf allows for more effective validation and optimization of 2D models. Full article

Assessing the condition of airport pavements is essential to ensure operational safety and efficiency. This study presents an innovative, fully automated approach to calculate the Pavement Condition Index (PCI) by combining UAV-based aerial photogrammetry with advanced Artificial Intelligence (AI) techniques. The method follows three key steps: first, analyzing orthophotos of individual pavement sections using a custom-trained AI model designed for precise crack detection and classification; second, utilizing skeletonization and semantic mask analysis to measure crack characteristics; and third, automating the PCI calculation for faster and more consistent evaluations. By leveraging high-resolution Unmanned Aerial Vehicle (UAV) imagery and advanced segmentation models, this approach achieves superior accuracy in detecting transverse and longitudinal cracks. The automated PCI calculation minimizes the need for human intervention, reduces errors, and supports more efficient, data-driven decision-making for airport pavement management. This study demonstrates the transformative potential of integrating UAV and AI technologies to facilitate infrastructure maintenance and enhance safety protocols. Full article

The characterization of dimensional stone deposits is essential for quarry assessment and design. However, uncertainties in mapping and designing pose significant challenges. To address this issue, an innovative approach is initiated to develop a virtual reality model by integrating unmanned aerial vehicle (UAV) photogrammetry for surface modeling and Electric Resistivity Tomography (ERT) for subsurface deposit imaging. This strategy offers a cost-effective, time-efficient, and safer alternative to traditional surveying methods for challenging mountainous terrain. UAV methodology involved data collection using a DJI Mavic 2 Pro (20 MP camera) with 4 K resolution images captured at 221 m altitude and 80 min flight duration. Images were taken with 75% frontal and 70% side overlaps. The Structure from Motion (SfM) processing chain generated high-resolution outputs, including point clouds, Digital Elevation Models (DEMs), Digital Surface Models (DSMs), and orthophotos. To ensure accuracy, five ground control points (GCPs) were established by a Real-Time Kinematic Global Navigation Satellite System (RTK GNSS). An ERT method known as vertical electric sounding (VES) revealed subsurface anomalies like solid rock mass, fractured zones and areas of iron leaching within marble deposits. Three Schlumberger (VES-1, 2, 3) and two parallel Wenner (VES-4, 5) arrays to a depth of 60 m were employed. The resistivity signature acquired by PASI RM1 was analyzed using 1D inversion technique software (ZondP1D). The integrated outputs of photogrammetry and subsurface imaging were used to design an optimized quarry with bench heights of 30 feet and widths of 50 feet, utilizing open-source 3D software (Blender, BIM, and InfraWorks). This integrated approach provides a comprehensive understanding of deposit surface and subsurface characteristics, facilitating optimized and sustainable quarry design and extraction. This research demonstrates the value of an innovative approach in synergistic integration of UAV photogrammetry and ERT, which are often used separately, for enhanced characterization, decision-making and promoting sustainable practices in dimensional stone deposits. Full article

Extracting accurate surface reflectance from multispectral UAV (unmanned aerial vehicle) imagery is a fundamental task in remote sensing. However, most studies have focused on short-endurance UAVs, with limited attention given to long-endurance UAVs due to the challenges posed by dynamically changing incident radiative energy. This study addresses this gap by employing a solar trajectory model (STM) to accurately estimate incident radiative energy, thereby improving reflectance calculation precision. The STM method addresses the following key issues: The experimental results demonstrated that the root mean square error (RMSE) of the STM method in Shanghai was 15.80% compared to the standard reflectance, which is 51% lower than the downwelling light sensor (DLS) method and 37% lower than the traditional method. This indicates that the STM method provides results that are more accurate, aligning closely with standard values. In Tianjin, the RMSE was 24% lower than the DLS method and 65% lower than the traditional method. The STM effectively mitigates inconsistencies in incident radiative energy across different image strips captured by long-endurance UAVs, ensuring uniform reflectance accuracy in digital orthophoto maps (DOMs). The proportion of corrected reflectance errors within the ideal range (±10%) increased by 24% compared to the histogram matching method. Furthermore, the optimal flight duration for long-endurance UAVs launched at noon was extended from 50 min to 150 min. In conclusion, this study demonstrates that applying the STM to correct multispectral imagery obtained from long-endurance UAVs significantly enhances reflectance calculation accuracy for DOMs, offering a practical solution for improving reflectance imagery quality under clear-sky conditions. Full article