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The number of two-dimensional surveying missions with unmanned aerial vehicles has dramatically increased in the last years. To fully automatize the surveying missions it is essential to solve the coverage path planning problem defined as the task of computing a path for a robot so that all the points of a region of interest will be observed. State-of-the-art planners define as the optimal path the one with the minimum number of flight lines. However, the connection path, composed by the path from the starting point to the region of interest plus the path from it to the ending point, is underestimated. We propose an efficient planner for computing the optimal edge-vertex back-and-forth path. Unlike previous approaches, we take into account the starting and ending points. In this article, we demonstrate the vertex-edge path optimality along with in-field experiments using a multirotor vehicle validating the applicability of the planner.

Bergner, J.; Wallin, D.; Yang, S., and Rybczyk, J., 2025. Using drone-captured imagery and a digital elevation model to differentiate eelgrass species: Padilla Bay, Washington. Journal of Coastal Research, 41(1), 105–121. Charlotte (North Carolina), ISSN 0749-0208. There are two primary species of eelgrass at the Padilla Bay National Estuarine Research Reserve, Zostera marina, a native eelgrass, and Zostera japonica, a nonnative. Recently, unoccupied aerial systems (UAS) have been used for eelgrass monitoring and mapping since imagery can be collected frequently and during different seasons. This project, conducted from April to September 2022, utilized UAS imagery, elevation data, and eelgrass vegetation surveys in the intertidal zone to identify regions with Z. japonica–dominant, mixed, and Z. marina–dominant cover. Multispectral imagery, using random forest (2000 trees) classification and eelgrass vegetation survey data, was used to predict eelgrass cover categories. Z. japonica–dominant, mixed, and Z. marina–dominant cover differed spectrally due to speciation and canopy characteristics, but low Z. japonica–dominant cover and exposed mud significantly decreased the accuracy in predicting that cover class in April and May. The overall accuracy predicting Z. japonica–dominant, mixed, and Z. marina–dominant cover was 75% using multispectral data alone. When multispectral imagery was combined with a 1-m-resolution digital elevation model (DEM) with a vertical error of 4.3 cm, the overall accuracy rose to 89%. Accuracy for each cover category rose as well. Most notably, Z. japonica–dominant cover rose from a user's accuracy of 71% to 92%. Z. japonica–dominant cover increased by 0.3 km2 from April to September. Mixed cover slightly increased from April to May, and Z. marina–dominant cover remained relatively consistent through the months. This is the first study to yield highly accurate classification between Z. japonica– and Z. marina–dominant cover, and results can be further improved through additional management of spectral variation.

The detection of asbestos roof slate by drone is necessary to avoid the safety risks and costs associated with visual inspection. Moreover, the use of deep-learning models increases the speed as well as reduces the cost of analyzing the images provided by the drone. In this study, we developed a comprehensive learning model using supervised and unsupervised classification techniques for the accurate classification of roof slate. We ensured the accuracy of our model using a low altitude of 100 m, which led to a ground sampling distance of 3 cm/pixel. Furthermore, we ensured that the model was comprehensive by including images captured under a variety of light and meteorological conditions and from a variety of angles. After applying the two classification methods to develop the learning dataset and employing the as-developed model for classification, 12 images were misclassified out of 475. Visual inspection and an adjustment of the classification system were performed, and the model was updated to precisely classify all 475 images. These results show that supervised and unsupervised classification can be used together to improve the accuracy of a deep-learning model for the detection of asbestos roof slate.

Drones are an effective tool for animal surveys, capable of generating an abundance of high‐quality ecological data. However, the large volume of ecological data generated introduces an additional problem of the requisite human resources to process and analyse such data. Deep learning models offer a solution to this challenge, capable of autonomously processing drone footage to detect animals with higher fidelity and lower latency when compared with humans. This work aimed to develop an animal detection architecture that classifies animals in accordance to their location (terrestrial vs. arboreal). The model incorporates human pilot inspired techniques for greater performance and consistency across time. Thermal drone footage across the state of New South Wales, Australia from surveys over a 2+ year period was used to construct a diverse training and validation dataset. A high‐resolution 3D simulation was developed to workload by autonomously generating labelled data to supplement manually labelled field data. The model was evaluated on 130 hours of thermal imagery (14 million images) containing 57 unique animal species where 1637 out of 1719 (95.23%) of human pilot recorded animals were detected. The model achieved an F1 score of 0.9410, a 4.36 percentage point increase in performance over a benchmark YOLOv8 model. Simulated data improved model performance by 1.7x for low data scenarios, lowering data labelling costs due to higher quality image pre‐labels. The proposed animal detection model demonstrates strong reporting accuracy in the detection and tracking of animals. The approach enables widespread adoption of drone‐capturing technology by providing in‐field real‐time assistance, allowing novice pilots to detect animals at the level of experienced pilots, whilst also reducing the burden of report generation and data labelling costs.

The 2021 eruption of the Tajogaite volcano (La Palma, Canary Islands) provided a unique opportunity to investigate the early post-eruptive magnetic structure of a newly formed volcanic edifice. Understanding these structures is essential for improving hazard assessment and risk mitigation strategies. In this study, we present the first high-resolution, drone-based aeromagnetic dataset over the Tajogaite volcano, aimed at clarifying its still-uncertain geodynamic framework at shallow depths. We describe the data acquisition and processing workflows for surveying volcanic terrains, providing insights into the challenges encountered and the methodologies applied. The magnetic dataset was analyzed and used to construct a 3D magnetic susceptibility model of the volcanic edifice and its surroundings. Our results revealed very low magnetic susceptibility values at very shallow depths (~50 m below the surface) over the main volcanic edifice, suggesting the presence of a likely vertical, dyke-like structure feeding the eruption. These findings indicate that these materials remain above their Curie temperature around two years after the eruption. Moreover, the magnetic anomalies display patterns that correlate with the previously inferred two-fault systems, which likely played a critical role in channelling magma toward the eruptive vents. An elongated zone of slightly low magnetic susceptibility was identified following the NE-SW Mazo fault orientation, extending toward the eruptive fissure. This feature was associated with a single, fault-controlled magma pathway that remained at high temperatures at the time of the survey, in agreement with studies in other volcanic environments. This study highlights the value of aeromagnetic surveys, particularly those conducted with drones, as effective tools for advancing our understanding of young and dynamic volcanic systems, especially regarding their shallow structures.

Understanding spatial and demographic patterns in threatened coastal sharks is essential for effective conservation, yet remote reef systems remain understudied due to logistical constraints. We used dual unmanned aerial vehicles (UAVs) to monitor juvenile Negaprion acutidens around Dongsha Atoll, a no-take marine reserve in the northern South China Sea. Thirteen synchronized UAV surveys were conducted during summer and winter, covering 20 locations categorized into three zones representing different levels of human impact. We quantified seasonal variation in shark abundance, body size, spatial distribution, and environmental drivers using Generalized Linear Mixed Models (GLMMs). Results revealed stable overall abundance but strong fine-scale shifts between summer and winter. Northeastern sites showed sharp declines in shark sightings during winter, likely due to monsoonal exposure, while sheltered southern zones supported increased winter presence. Neonates were concentrated in lagoon habitats, whereas larger individuals occurred farther offshore and in seagrass areas, indicating ontogenetic habitat expansion. Human impact shaped demographic structure: low-impact areas hosted more and larger sharks, while smaller individuals and nearshore aggregation dominated high-impact zones. These findings confirm Dongsha’s role as a critical nursery habitat for N. acutidens and highlight the utility of UAV surveys for capturing spatiotemporal ecological patterns in remote ecosystems. By integrating UAV monitoring with conservation planning, managers can prioritize seasonal protection of nursery zones and respond adaptively to climate and anthropogenic pressures.

This research presents a method in assessing the impact of Ground Control Point (GCP) distribution, quantity, and inter-GCP distances on the output Digital Elevation Model (DEM) by utilizing SfM and GIS. The study was carried out in a quarry site to assess the impacts of these parameters on the accuracy of accurate volumetric measurements UAV derivatives. Based on GCP Root Mean Square Error (RMSE) and surface checkpoint error (SCE), results showed that the best configuration is the evenly distributed GCP set (1.58 m average RMSE, 1.30 m average SCE). Configurations clumped to edge and distributed to edge follow suit with respective RMSE (SCE) of 2.53 m (2.13 m) and 3.11 m (2.54 m). The clumped to center configuration yielded 6.23 m RMSE and 4.66 m SCE. As the number of GCPs used increase, the RMSE and SCE are observed to decrease consistently for all configurations. Further iteration of the best configuration showed that from RMSE of 4.11 m when 4 GCPs are used, there is a drastic decrease to 0.86 m once 10 GCPs are used. From that quantity, only centimeter differences can be observed until the full set of 24 GCPs have been used with a 0.012 m error. This is reflected in the stockpile measurement when the iteration results are compared to the reference data. The dataset processed with a minimum of 4 GCPs have a 606,991.43 m3 difference, whereas the dataset processed with 23 out of 24 has a 791.12 m3 difference from the reference data. The accuracy of the SfM-based DEM increases with the quantity of the GCPs used with an even distribution.

The Tunguska event of 1908 remains the most significant atmospheric explosion in recorded history, yet its geophysical effects, particularly its impact on Earth's magnetic field, remain uncertain. This study presents the first detailed magnetometer survey of the Tunguska epicenter, aiming to map regional magnetic anomalies and assess potential impact‐induced magnetization. The survey used unmanned aerial vehicle and covered approximately 30 square kilometers, revealing a complex pattern of magnetic anomalies that correlate with known geological structures. Notably, some anomalies exhibit spatial alignment with the presumed trajectory of the airburst (∼300° azimuth), suggesting potential influence from the event. This spatial correlation raises the possibility that transient electromagnetic effects from the airburst, such as ionization‐induced remagnetization or shock‐induced changes in magnetic mineralogy, could have contributed to the observed anomaly distribution. However, due to the limitations of our data set, we cannot definitively attribute any observed anomalies to impact‐related remagnetization. Our analysis identifies regions where future rock magnetic studies could provide further insights. We discuss possible mechanisms for transient remagnetization, including ionization effects and shock‐induced mineral transformations, while emphasizing the necessity of future paleomagnetic sampling to test these hypotheses. These findings establish a foundational geophysical data set for future interdisciplinary investigations into the Tunguska event's environmental and geological consequences. Plain Language Summary Our study investigates the Tunguska event, a massive explosion that occurred in Siberia in 1908, likely caused by an asteroid or comet entering Earth's atmosphere. This event is the largest cosmic impact recorded in human history, but its origins and effects are still not fully understood. We used drones equipped with sensitive magnetic sensors to create detailed maps of variations in Earth's magnetic field around the area where the explosion occurred. These variations, or “anomalies,” can give us clues about the underlying rock structures and potential changes caused by the cosmic event. Some of the magnetic patterns we observed line up with the path we believe the object took as it entered the atmosphere. This suggests the explosion might have altered the magnetic properties of the local rocks and soil. Our findings not only help us understand more about the Tunguska event but also provide insights into how such cosmic impacts can affect Earth's surface. This research could help identify and study other impact sites on Earth and potentially on other planets. Key Points Unmanned aerial vehicle (UAV)‐based magnetic survey identifies previously unrecognized anomalies at the Tunguska site Magnetic anomalies at the Tunguska site align with the presumed impact trajectory Innovative use of UAVs demonstrates the effectiveness of airborne magnetometry in surveying remote and inaccessible regions

The use of Unmanned Aerial Vehicles (UAVs) represents a rather innovative, quick, and low-cost methodological approach offering applications in several fields of investigation. The present study illustrates the developed method using Digital Elevation Models (DEMs) based on UAV-derived data for evaluating short-term morphological-topographic changes of the beach system and related implications for coastal vulnerability assessment. UAV surveys were performed during the summers of 2019 and 2020 along a beach stretch affected by erosion, located along the central Adriatic coast. Acquired high-resolution aerial photos were used to generate large-scale DEMs as well as orthophotos of the beach using the Structure from Motion (SfM) image processing tool. Comparison of the generated 2019 and 2020 DEMs highlighted significant morphological changes and a sediment volume loss of about 780 m3 within a surface area of about 4400 m2. Based on 20 m spaced beach profiles derived from the DEMs, a coastal vulnerability assessment was performed using the CVA approach that highlighted some significant variations in the CVA index between 2019 and 2020. Results evidence that UAV surveys provide high-resolution topographic data, suitable for specific beach monitoring activities and the updating of some parameters that enter in the CVA model contributing to its correct application.

Walking behavior is influenced by both objective and subjective aspects of the built environment at the macro and micro scales. Most walkability studies focused on objective macro or mesoscale variables. The few studies that included microlevel indicators used various methods and sources to quantify street level urban design features, each with its own limitations. This study used drone photogrammetry to capture street features in a rapidly urbanizing area in the Philippines and showed that observational, distance, and view-related types of measurement can be done using a single 3D model. An inter-rater reliability test was conducted for observational indicators and showed good to excellent reliability. Using the quantified street features, we tested its correlation with scores generated from a walker perception survey to develop a composite walkability index that can be used for urban design and planning. Results showed that 13 walkability sub-models are statistically significant, wherein models pertaining to safety assumed the highest weights while complexity and imageability models ranked lowest. This study validated many of the street level indicators previously reported, while also suggested new ones. For some indicators, model effects were opposite of what was previously reported such as number of people, buildings with non-rectangular silhouettes and view of sky across, which reflect the unique characteristics of the study area. Findings provide new insights on walkability which may lead to improvements in the pedestrian environment, especially in the context of developing countries.