Search Results (517)

Residential green space is an important component of urban green space and one of the major indicators for evaluating the quality of a residential community. Traditional indicators such as the green space ratio only consider the relationship between green space area and total area of the residential community while ignoring the difference in the amount of green space enjoyed by household residents in high-rise and low-rise buildings. Therefore, it is meaningful to estimate household green space and its spatial distribution in residential communities. However, there are frequent difficulties in obtaining specific green space area and household number through ground surveys or consulting with property management units. In this study, taking a composite residential community in Chongqing, China, as the study site, we first employed a five-lens drone to capture its oblique RGB images and generated the DOM (Digital Orthophoto Map). Subsequently, the green space area and distribution in the entire residential community were extracted from the DOM using VDVI (Visible Difference Vegetation Index). The YOLACT (You Only Look At Coefficients) instance segmentation model was used to recognize balconies from the facade images of high-rise buildings to determine their household numbers. Finally, the average green space per household in the entire residential community was calculated to be 67.82 m², and those in the high-rise and low-rise building zones were 51.28 m² and 300 m², respectively. Compared with the green space ratios of 65.5% and 50%, household green space more truly reflected the actual green space occupation in high- and low-rise building zones. Full article

UAV platforms equipped with RTK positioning and LiDAR sensors are increasingly used for landslide monitoring, offering frequent, high-resolution surveys with broad spatial coverage. In this study, we applied high-frequency UAV-based monitoring to the active Baldiola earthflow (Northern Apennines, Italy), integrating 10 UAV–LiDAR and photogrammetric surveys, acquired at average intervals of 14 days over a four-month period. UAV-derived orthophotos and DEMs supported displacement analysis through homologous point tracking (HPT), with robotic total station measurements serving as ground-truth data for validation. DEMs were also used for multi-temporal DEM of Difference (DoD) analysis to assess elevation changes and identify depletion and accumulation patterns. Displacement trends derived from HPT showed strong agreement with RTS data in both horizontal (R² = 0.98) and vertical (R² = 0.94) components, with cumulative displacements ranging from 2 m to over 40 m between April and August 2024. DoD analysis further supported the interpretation of slope processes, revealing sector-specific reactivations and material redistribution. UAV-based monitoring provided accurate displacement measurements, operational flexibility, and spatially complete datasets, supporting its use as a reliable and scalable tool for landslide analysis. The results support its potential as a stand-alone solution for both monitoring and emergency response applications. Full article

The equator’s unique combination of high humidity and temperature renders optical satellite imagery highly susceptible to persistent cloud cover. In contrast, synthetic aperture radar (SAR) offers a robust alternative due to its ability to penetrate clouds with microwave imaging. This study addresses the challenges of cloud-induced data gaps and cross-sensor geometric biases by proposing an advanced optical and SAR image-matching framework specifically designed for cloud-prone equatorial regions. We use a prompt-driven visual segmentation model with automatic prompt point generation to produce cloud masks that guide cross-modal feature-matching and joint adjustment of optical and SAR data. This process results in a comprehensive digital orthophoto map (DOM) with high geometric consistency, retaining the fine spatial detail of optical data and the all-weather reliability of SAR. We validate our approach across four equatorial regions using five satellite platforms with varying spatial resolutions and revisit intervals. Even in areas with more than 50 percent cloud cover, our method maintains sub-pixel edging accuracy under manual check points and delivers comprehensive DOM products, establishing a reliable foundation for downstream environmental monitoring and ecosystem analysis. Full article

This study examined the integraftion of airborne Light Detection and Ranging (LiDAR), Simultaneous Localization and Mapping (SLAM)-based handheld LiDAR, and iPhone LiDAR to inspect forest road networks following forest operations. The goal is to overcome the challenges posed by dense canopy cover and ensure accurate and efficient data collection and mapping. Airborne data were collected using the DJI Matrice 300 RTK UAV equipped with a Zenmuse L2 LiDAR sensor, which achieved a high point density of 285 points/m² at an altitude of 80 m. Ground-level data were collected using the BLK2GO handheld laser scanner (HPLS) with SLAM methods (LiDAR SLAM, Visual SLAM, Inertial Measurement Unit) and the iPhone 13 Pro Max LiDAR. Data processing included generating DEMs, DSMs, and True Digital Orthophotos (TDOMs) via DJI Terra, LiDAR360 V8, and Cyclone REGISTER 360 PLUS, with additional processing and merging using CloudCompare V2 and ArcGIS Pro 3.4.0. The pairwise comparison analysis between ALS data and each alternative method revealed notable differences in elevation, highlighting discrepancies between methods. ALS + iPhone demonstrated the smallest deviation from ALS (MAE = 0.011, RMSE = 0.011, RE = 0.003%) and HPLS the larger deviation from ALS (MAE = 0.507, RMSE = 0.542, RE = 0.123%). The findings highlight the potential of fusing point clouds from diverse platforms to enhance forest road mapping accuracy. However, the selection of technology should consider trade-offs among accuracy, cost, and operational constraints. Mobile LiDAR solutions, particularly the iPhone, offer promising low-cost alternatives for certain applications. Future research should explore real-time fusion workflows and strategies to improve the cost-effectiveness and scalability of multisensor approaches for forest road monitoring. Full article

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

In response to the inability of traditional remote sensing technology to accurately capture the micro-topographic features of landslide surfaces in vegetated areas under complex terrain conditions, this paper proposes a method for enhanced landslide terrain display and trace recognition based on airborne LiDAR technology. Firstly, a high-precision LiDAR-DEM is constructed using preprocessed LiDAR point cloud data, and visual images are generated using visualization methods, including hillshade, slope, openness, and Sky View Factor (SVF). Secondly, pixel-level image fusion methods are applied to the visual images to obtain enhanced display images of the landslide terrain. Finally, a threshold is determined through a fractal model, and the Mean-Shift algorithm is utilized for clustering and denoising to extract landslide traces. The results indicate that employing pixel-level image fusion technology, which combines the advantageous features of multiple terrain visualization images, effectively enhances the display of landslide micro-topography. Moreover, based on the enhanced display images, the fractal model and the Mean-Shift algorithm are applied for denoising to extract landslide traces. Compared to orthophotos, this method can effectively and accurately extract landslide traces. The findings of this study provide valuable references for the enhanced display and trace recognition of landslide terrain in densely vegetated areas within complex mountainous areas, thereby providing technical support for emergency investigations of landslide disasters. Full article

The downwelling light sensor (DLS) is the industry-standard solution for generating UAV-based digital orthophoto maps (DOMs). Current mainstream DLS correction methods primarily rely on angle compensation. However, due to the temporal mismatch between the DLS sampling intervals and the exposure times of multispectral cameras, as well as external disturbances such as strong wind gusts and abrupt changes in flight attitude, DLS data often become unreliable, particularly at UAV turning points. Building upon traditional angle compensation methods, this study proposes an improved correction approach—FIM-DC (Fitting and Interpolation Model-based Data Correction)—specifically designed for data collection under clear-sky conditions and stable atmospheric illumination, with the goal of significantly enhancing the accuracy of reflectance retrieval. The method addresses three key issues: (1) field tests conducted in the Qingpu region show that FIM-DC markedly reduces the standard deviation of reflectance at tie points across multiple spectral bands and flight sessions, with the most substantial reduction from 15.07% to 0.58%; (2) it effectively mitigates inconsistencies in reflectance within image mosaics caused by anomalous DLS readings, thereby improving the uniformity of DOMs; and (3) FIM-DC accurately corrects the spectral curves of six land cover types in anomalous images, making them consistent with those from non-anomalous images. In summary, this study demonstrates that integrating FIM-DC into DLS data correction workflows for UAV-based multispectral imagery significantly enhances reflectance calculation accuracy and provides a robust solution for improving image quality under stable illumination conditions. Full article

Aging port infrastructure demands frequent and reliable inspections, yet the existing automated systems often require expensive industrial drones, posing significant adoption barriers for local governments with limited resources. To address this challenge, this study develops a low-cost, automated crack detection system for port quay walls utilizing orthophotos generated from a small general-purpose drone. The system employs the YOLOR (You Only Learn One Representation) object detection algorithm, enhanced by two novel image processing techniques—overlapping tiling and pseudo-altitude slicing—to overcome the resolution limitations of low-cost cameras. While official guidelines for port facilities designate 3 mm as an inspection threshold, our system is specifically designed to achieve a higher-resolution detection capability for cracks as narrow as 1 mm. This approach ensures reliable detection with a sufficient safety margin and enables the proactive monitoring of crack progression for preventive maintenance. The effectiveness of the proposed image processing techniques was validated, with an F₁ score-based analysis revealing key trade-offs between maximizing detection recall and achieving a balanced performance depending on the chosen simulated altitude. Furthermore, evaluation using real-world inspection data demonstrated that the proposed system achieves a detection performance comparable to that of a well-established commercial system, confirming its practical applicability. Crucially, by mapping the detected cracks to real-world coordinates on georeferenced orthophotos, the system provides a foundation for advanced, data-driven asset management, allowing for the quantitative tracking of deterioration over time. These results confirm that the proposed workflow is a practical and sustainable solution for infrastructure monitoring. 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

In the field of building archaeology, the analysis of wall surfaces represents a fundamental tool for the study of an architecture and its construction phases. In fact, masonry stores valuable information regarding not only used materials and construction techniques but also transformations happen over time for natural events or anthropic interventions. The traditional approach to the analysis of building materials is mainly based on direct observation and manual annotations based on orthophotos obtained through photogrammetric surveys. This process, while providing a high degree of accuracy and understanding, is extremely time- and resource-consuming. In addition, the lack of standardised procedures for the statistical analysis of measurements leads to data that are difficult to compare for different contexts. Time and subjectivity are ultimately the two main limitations that most hinder the diffusion of the mensiochronological approach and for this reason, the most recent artificial intelligence solutions for the segmentation and extraction of measurements of individual masonry components will be addressed. Finally, a workflow will be presented based on image segmentation using machine learning models and the automatic extraction and statistical analysis of measurements using a script designed specifically by the author for the mensiochronological analysis of Roman brick masonry. Full article